A deployment method and system for vehicle type identification equipment

Abstract

The present invention relates to the field of data processing technology, and discloses a method and system for deploying vehicle type recognition equipment, the method comprising: determining a set of identification quantity parameters for each type of vehicle type recognition equipment among multiple types of vehicle type recognition equipment; calculating an index set for each type of vehicle type recognition equipment according to the set of identification quantity parameters for all vehicle type recognition equipment; screening out target vehicle type recognition equipment that meets preset rating conditions from all vehicle type recognition equipment according to the index set for all vehicle type recognition equipment; determining at least one deployment point of the target vehicle type recognition equipment from the target lane area to be deployed according to the target identification quantity parameter set for the target vehicle type recognition equipment. It can be seen that the implementation of the present invention can improve the deployment accuracy of vehicle type recognition equipment, thereby facilitating the reduction of vehicle missed recognition / misrecognition based on accurately deployed vehicle type recognition equipment, and further facilitating the reduction of the loss of lane tolls.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a deployment method and system for vehicle type identification equipment. Background Art

[0002] At present, the main method of charging vehicle tolls is to charge according to the vehicle model and the actual road section of the vehicle. The identification of vehicle models often requires the use of model identification equipment, such as vehicle ETC equipment, model identifier, OBD diagnostic instrument and vehicle identification number (VIN) parser, etc., and the classification standards of vehicle models are mainly: for passenger cars and ordinary trucks, the model issued by the vehicle ETC equipment shall prevail; for truck trains, container trucks, large-scale transport trucks and tractors, the model identified by the model identification equipment at the lane entrance when the vehicle enters the lane entrance shall prevail.

[0003] However, practice has found that the current deployment accuracy of vehicle type recognition equipment in lanes is low, which can easily lead to the missed or misidentified models of some vehicles, resulting in the loss of tolls in the lane. It can be seen that it is particularly important to propose a technical solution to improve the deployment accuracy of vehicle type recognition equipment. Summary of the invention

[0004] The present invention provides a method and system for deploying vehicle type recognition equipment, which can improve the deployment accuracy of vehicle type recognition equipment, help reduce the occurrence of missed / misidentified vehicles based on accurately deployed vehicle type recognition equipment, and help reduce the loss of lane tolls.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a method for deploying a vehicle type identification device, the method comprising:

[0006] Determine a recognition quantity parameter set for each type of vehicle type recognition device in the plurality of types of vehicle type recognition devices, wherein the recognition quantity parameter set includes one or more recognition quantity parameters, and each recognition quantity parameter includes one of the number of recognized vehicles, the number of vehicle head captures, the number of correct license plate type matches, the number of correct license plate recognitions, and the number of accurate vehicle type recognitions;

[0007] Calculate an index set for each type of vehicle identification device according to the identification quantity parameter set of all the vehicle identification devices, wherein the index set includes at least one of vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle model recognition accuracy, and device log completeness rate;

[0008] According to the index set of all the vehicle type identification devices, a target vehicle type identification device that meets a preset rating condition is selected from all the vehicle type identification devices;

[0009] According to the target recognition quantity parameter set of the target vehicle type recognition device, at least one deployment point of the target vehicle type recognition device is determined from the target lane area to be deployed, and the location of the deployment point is used to deploy at least one target vehicle type recognition device.

[0010] As an optional implementation, in the first aspect of the present invention, the identification quantity parameter set of each type of vehicle type identification device is obtained by analyzing the test flow information of the vehicle type identification device of this type, and the test flow information includes the flow information of the corresponding vehicle type identification device for the same test lane area, and the test flow information includes the identification quantity of the test vehicle, and also includes the flow number, flow type, audit status, detection time, license plate recognition information, vehicle head recognition information and vehicle type recognition information of each test vehicle. One or more combinations thereof, each type of vehicle type identification device includes at least one vehicle type identification device;

[0011] And, calculating the index set of each type of vehicle type identification device according to the identification quantity parameter set of all the vehicle type identification devices includes:

[0012] Calculating the actual number of vehicles in the test lane area according to the identification quantity parameter set of all the vehicle type identification devices;

[0013] Calculate the index value corresponding to each identification quantity parameter of each type of vehicle identification device according to the actual number of vehicles, each identification quantity parameter of each type of vehicle identification device, and the index calculation method corresponding to each identification quantity parameter;

[0014] The index values ​​corresponding to all the identification quantity parameters of each type of vehicle type identification device are combined to obtain an index set for each type of vehicle type identification device.

[0015] As an optional implementation, in the first aspect of the present invention, the actual number of vehicles in the test lane area is calculated according to the identification number parameter set of all the vehicle type identification devices, including:

[0016] According to the identification quantity parameter sets of all the vehicle type identification devices, selecting a target identification quantity parameter set from all the identification quantity parameter sets, wherein the target identification quantity parameter set includes at least a first identification quantity parameter set and a second identification quantity parameter set;

[0017] Comparing the first identification quantity parameter set and the second identification quantity parameter set to obtain a comparison result;

[0018] According to the comparison result, the first identification quantity parameter set and the second identification quantity parameter set are integrated to obtain an integrated identification quantity parameter set;

[0019] The test vehicles in the test lane area are analyzed according to the integrated set of identification quantity parameters to obtain the actual number of vehicles in the test lane area.

[0020] As an optional implementation, in the first aspect of the present invention, selecting a target recognition quantity parameter set from all the recognition quantity parameter sets according to the recognition quantity parameter sets of all the vehicle type recognition devices comprises:

[0021] Calculating the identification quantity parameter sets of all the vehicle type identification devices to obtain a plurality of parameter differences, each of which is used to represent the difference between the identification quantity parameter sets of any two of the vehicle type identification devices;

[0022] A target parameter difference greater than or equal to a preset difference is selected from all the parameter differences, and a recognition quantity parameter set of all vehicle type recognition devices corresponding to the target parameter difference is determined as a target recognition quantity parameter set.

[0023] As an optional implementation, in the first aspect of the present invention, determining at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device includes:

[0024] Analyzing the flow of each of the plurality of vehicle types identified by the target vehicle type identification device according to the target identification quantity parameter set of the target vehicle type identification device;

[0025] According to a preset vehicle type identification method, all the vehicle types are classified to obtain a classification result, wherein the vehicle type identification method includes a first vehicle type identification method and a second vehicle type identification method, and the classification result includes a first type vehicle type set and a second type vehicle type set, wherein the first type vehicle type set is empty or all first type vehicles in the first type vehicle type set are identified by the first type vehicle type identification method, the second type vehicle type set is empty or all second type vehicles in the second type vehicle type set are identified by the second type vehicle type identification method, and each second type vehicle type has a corresponding identification location;

[0026] According to the traffic of all the second-category vehicles included in the classification result, the identification locations corresponding to all the second-category vehicles are screened to obtain at least one target identification location, and the target identification location is determined as the deployment point of the target vehicle model identification device.

[0027] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0028] After determining the deployment points in the target lane area, obtaining environmental information of each deployment point and traffic flow information of each deployment point;

[0029] Determine the equipment deployment requirements of each deployment point according to the environmental information of each deployment point and the traffic flow information of each deployment point, wherein the equipment deployment requirements include the deployment quantity requirements of the target vehicle type identification devices in the corresponding deployment point and the deployment association relationship between each target vehicle type identification device;

[0030] According to the equipment deployment requirements of each of the deployment points, formulate an equipment deployment plan that matches each of the deployment points.

[0031] As an optional implementation, in the first aspect of the present invention, the step of selecting a target vehicle type identification device that meets a preset rating condition from all the vehicle type identification devices according to the indicator set of all the vehicle type identification devices includes:

[0032] According to the index set of each type of vehicle identification device, each type of vehicle identification device is comprehensively rated to obtain a rating result of each type of vehicle identification device, wherein the rating result is used to represent the index level of the corresponding vehicle identification device;

[0033] According to the rating results of all the vehicle type recognition devices, a target vehicle type recognition device that meets a preset rating condition is determined from all the vehicle type recognition devices, wherein the preset rating condition includes a condition that the indicator level corresponds to a preset indicator level;

[0034] Wherein, the step of determining a target vehicle type identification device that meets a preset rating condition from all the vehicle type identification devices according to the rating results of all the vehicle type identification devices comprises:

[0035] For each type of vehicle type identification device, determining whether the index level represented by the rating result of the vehicle type identification device of this type is a preset index level;

[0036] If it is determined that the index level of the vehicle type identification device of this type is not the preset index level, then the vehicle type identification device of this type is excluded;

[0037] If it is determined that the index level of the type of vehicle identification device is the preset index level, the type of vehicle identification device is determined to be the target vehicle identification device.

[0038] A second aspect of the present invention discloses a deployment system for vehicle type identification equipment, the system comprising a determination module, a calculation module and a screening module, wherein:

[0039] The determination module is used to determine a set of identification quantity parameters for each type of vehicle type identification device in the plurality of types of vehicle type identification devices, wherein the set of identification quantity parameters includes one or more identification quantity parameters, and each of the identification quantity parameters includes one of the number of identified vehicles, the number of captured vehicle heads, the number of correct license plate matches, the number of correct license plate identifications, and the number of accurate vehicle type identifications;

[0040] The calculation module is used to calculate an index set of each type of vehicle identification device according to the identification quantity parameter set of all the vehicle identification devices, and the index set includes at least one of vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle model recognition accuracy and equipment log completeness rate;

[0041] The screening module is used to screen out target vehicle type identification devices that meet preset rating conditions from all the vehicle type identification devices according to the indicator set of all the vehicle type identification devices;

[0042] The determination module is also used to determine at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device, and the location of the deployment point is used to deploy at least one target vehicle type recognition device.

[0043] As an optional implementation, in the second aspect of the present invention, the identification quantity parameter set of each type of vehicle type identification device is obtained by analyzing the test flow information of the vehicle type identification device of this type, and the test flow information includes the flow information of the corresponding vehicle type identification device for the same test lane area, and the test flow information includes the identification quantity of the test vehicle, and also includes the flow number, flow type, audit status, detection time, license plate recognition information, vehicle head recognition information and vehicle type recognition information of each test vehicle. One or more combinations of each type of vehicle type identification device include at least one vehicle type identification device;

[0044] Furthermore, the calculation module calculates the index set of each type of vehicle type identification device according to the identification quantity parameter set of all the vehicle type identification devices, specifically including:

[0045] Calculating the actual number of vehicles in the test lane area according to the identification quantity parameter set of all the vehicle type identification devices;

[0046] Calculate the index value corresponding to each identification quantity parameter of each type of vehicle identification device according to the actual number of vehicles, each identification quantity parameter of each type of vehicle identification device, and the index calculation method corresponding to each identification quantity parameter;

[0047] The index values ​​corresponding to all the identification quantity parameters of each type of vehicle type identification device are combined to obtain an index set for each type of vehicle type identification device.

[0048] As an optional implementation, in the second aspect of the present invention, the calculation module calculates the actual number of vehicles in the test lane area according to the identification number parameter set of all the vehicle type identification devices, specifically including:

[0049] According to the identification quantity parameter sets of all the vehicle type identification devices, selecting a target identification quantity parameter set from all the identification quantity parameter sets, wherein the target identification quantity parameter set includes at least a first identification quantity parameter set and a second identification quantity parameter set;

[0050] Comparing the first identification quantity parameter set and the second identification quantity parameter set to obtain a comparison result;

[0051] According to the comparison result, the first identification quantity parameter set and the second identification quantity parameter set are integrated to obtain an integrated identification quantity parameter set;

[0052] The test vehicles in the test lane area are analyzed according to the integrated set of identification quantity parameters to obtain the actual number of vehicles in the test lane area.

[0053] As an optional implementation, in the second aspect of the present invention, the calculation module selects a target recognition quantity parameter set from all the recognition quantity parameter sets according to the recognition quantity parameter sets of all the vehicle type recognition devices, specifically including:

[0054] Calculating the identification quantity parameter sets of all the vehicle type identification devices to obtain a plurality of parameter differences, each of which is used to represent the difference between the identification quantity parameter sets of any two of the vehicle type identification devices;

[0055] A target parameter difference greater than or equal to a preset difference is selected from all the parameter differences, and a recognition quantity parameter set of all vehicle type recognition devices corresponding to the target parameter difference is determined as a target recognition quantity parameter set.

[0056] As an optional implementation, in the second aspect of the present invention, the determination module determines at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device, specifically including:

[0057] Analyzing the flow of each of the plurality of vehicle types identified by the target vehicle type identification device according to the target identification quantity parameter set of the target vehicle type identification device;

[0058] According to a preset vehicle type identification method, all the vehicle types are classified to obtain a classification result, wherein the vehicle type identification method includes a first vehicle type identification method and a second vehicle type identification method, and the classification result includes a first type vehicle type set and a second type vehicle type set, wherein the first type vehicle type set is empty or all first type vehicles in the first type vehicle type set are identified by the first type vehicle type identification method, the second type vehicle type set is empty or all second type vehicles in the second type vehicle type set are identified by the second type vehicle type identification method, and each second type vehicle type has a corresponding identification location;

[0059] According to the traffic of all the second-category vehicles included in the classification result, the identification locations corresponding to all the second-category vehicles are screened to obtain at least one target identification location, and the target identification location is determined as the deployment point of the target vehicle model identification device.

[0060] As an optional implementation, in the second aspect of the present invention, the system further includes an acquisition module and a formulation module, wherein:

[0061] The acquisition module is used to acquire the environmental information of each deployment point and the traffic flow information of each deployment point after the determination module determines the deployment points of the target lane area;

[0062] The determination module is further used to determine the equipment deployment requirements of each deployment point according to the environmental information of each deployment point and the traffic flow information of each deployment point, wherein the equipment deployment requirements include the deployment quantity requirements of the target vehicle type identification devices in the corresponding deployment point and the deployment association relationship between each target vehicle type identification device;

[0063] The formulation module is used to formulate an equipment deployment plan matching each of the deployment points according to the equipment deployment requirements of each of the deployment points.

[0064] As an optional implementation, in the second aspect of the present invention, the screening module selects the target vehicle type identification device that meets the preset rating condition from all the vehicle type identification devices according to the indicator set of all the vehicle type identification devices, specifically including:

[0065] According to the index set of each type of vehicle identification device, each type of vehicle identification device is comprehensively rated to obtain a rating result of each type of vehicle identification device, wherein the rating result is used to represent the index level of the corresponding vehicle identification device;

[0066] According to the rating results of all the vehicle type recognition devices, a target vehicle type recognition device that meets a preset rating condition is determined from all the vehicle type recognition devices, wherein the preset rating condition includes a condition that the indicator level corresponds to a preset indicator level;

[0067] The method in which the screening module determines the target vehicle type identification device that meets the preset rating condition from all the vehicle type identification devices according to the rating results of all the vehicle type identification devices specifically includes:

[0068] For each type of vehicle type identification device, determining whether the index level represented by the rating result of the vehicle type identification device of this type is a preset index level;

[0069] If it is determined that the index level of the vehicle type identification device of this type is not the preset index level, then the vehicle type identification device of this type is excluded;

[0070] If it is determined that the index level of the type of vehicle identification device is the preset index level, the type of vehicle identification device is determined to be the target vehicle identification device.

[0071] A third aspect of the present invention discloses another deployment system for vehicle type identification equipment, the system comprising:

[0072] A memory storing executable program code;

[0073] a processor coupled to the memory;

[0074] The processor calls the executable program code stored in the memory to execute the deployment method of the vehicle type identification device disclosed in the first aspect of the present invention.

[0075] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the deployment method of the vehicle model identification device disclosed in the first aspect of the present invention.

[0076] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0077] In an embodiment of the present invention, a recognition quantity parameter set of each type of vehicle identification device among multiple types of vehicle identification devices is determined, the recognition quantity parameter set includes one or more recognition quantity parameters, and each recognition quantity parameter includes one of the number of identified vehicles, the number of vehicle head captures, the number of correct license plate matches, the number of correct license plate recognitions, and the number of accurate vehicle model recognitions; based on the recognition quantity parameter set of all vehicle model identification devices, an index set of each type of vehicle model identification device is calculated, the index set includes at least one of vehicle counting detection accuracy, vehicle capture rate, license plate match accuracy, license plate recognition accuracy, vehicle model recognition accuracy, and device log completeness rate; based on the index set of all vehicle model identification devices, a target vehicle model identification device that meets a preset rating condition is screened out from all vehicle model identification devices; based on the target recognition quantity parameter set of the target vehicle model identification device, at least one deployment point of the target vehicle model identification device is determined from the target lane area to be deployed, and the location of the deployment point is used to deploy at least one target vehicle model identification device. It can be seen that the implementation of the present invention can determine the identification quantity parameter set of each type of vehicle identification device in multiple types of vehicle identification devices, and calculate the index set of each type of vehicle identification device based on the identification quantity parameter set of all vehicle identification devices, which can improve the calculation accuracy of the index set of each type of vehicle identification device based on the identification quantity parameter set with parameter diversity and can improve the content richness of the index set. Subsequently, based on the accurately calculated index set of all vehicle identification devices, the target vehicle identification device that meets the preset rating conditions is screened out from all vehicle identification devices, which can improve the screening accuracy of the target vehicle identification device. Then, based on the target identification quantity parameter set of the accurately screened target vehicle identification device, at least one deployment point of the target vehicle identification device is determined from the target lane area to be deployed, which can improve the determination accuracy of the deployment point of the target vehicle identification device and improve the deployment accuracy of the target vehicle identification device, thereby facilitating the reduction of missed / misidentified vehicle identification based on the accurately deployed target vehicle identification device, and further facilitating the reduction of the loss of lane tolls. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 It is a flow chart of a method for deploying a vehicle type identification device disclosed in an embodiment of the present invention;

[0080] Figure 2It is a flow chart of another method for deploying a vehicle type identification device disclosed in an embodiment of the present invention;

[0081] Figure 3 It is a structural schematic diagram of a deployment system of a vehicle type identification device disclosed in an embodiment of the present invention;

[0082] Figure 4 It is a structural schematic diagram of another deployment system of a vehicle type identification device disclosed in an embodiment of the present invention;

[0083] Figure 5 It is a structural schematic diagram of another deployment system of a vehicle type identification device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or terminal including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or terminals.

[0086] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0087] The present invention discloses a method and system for deploying vehicle type recognition equipment, which can determine the identification quantity parameter set of each type of vehicle type recognition equipment among multiple types of vehicle type recognition equipment, and calculate the index set of each type of vehicle type recognition equipment based on the identification quantity parameter set of all vehicle type recognition equipment, which can improve the calculation accuracy of the index set of each type of vehicle type recognition equipment based on the identification quantity parameter set of parameter diversity and improve the content richness of the index set, and then screen out target vehicle type recognition equipment that meets preset rating conditions from all vehicle type recognition equipment according to the accurately calculated index set of all vehicle type recognition equipment, which can improve the screening accuracy of the target vehicle type recognition equipment, and then determine at least one deployment point of the target vehicle type recognition equipment from the target lane area to be deployed according to the target identification quantity parameter set of the accurately screened target vehicle type recognition equipment, which can improve the determination accuracy of the deployment point of the target vehicle type recognition equipment and improve the deployment accuracy of the target vehicle type recognition equipment, thereby facilitating the reduction of vehicle missed recognition / misrecognition based on the accurately deployed target vehicle type recognition equipment, and further facilitating the reduction of the loss of lane tolls. The following are detailed descriptions.

[0088] Embodiment 1

[0089] See also Figure 1 , Figure 1 1 is a flow chart of a method for deploying a vehicle type identification device disclosed in an embodiment of the present invention. Figure 1 The deployment method of the vehicle type identification device described above can be applied to a deployment system of the vehicle type identification device, wherein the system may include a deployment device or a deployment server, wherein the deployment server may include a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the deployment method of the vehicle type identification device may include the following operations:

[0090] 101. Determine a set of identification quantity parameters for each type of vehicle type identification device among multiple types of vehicle type identification devices.

[0091] In an embodiment of the present invention, the identification quantity parameter set may include one or more identification quantity parameters. Among them, each identification quantity parameter may include one of the number of identified vehicles identified by the corresponding type of vehicle type identification device, the number of vehicle head captures identified by the corresponding type of vehicle type identification device, the number of correct license plate matches identified by the corresponding type of vehicle type identification device, the number of correct license plate recognition identified by the corresponding type of vehicle type identification device, and the number of accurate vehicle type recognition identified by the corresponding type of vehicle type identification device. Among them, the number of vehicle head captures is the number of real vehicles and vehicle heads captured in the vehicle head picture captured by the system corresponding to the corresponding vehicle type identification device. The number of correct license plate matches is the number of correct vehicle heads and vehicle sides matched in the composite picture output by the system corresponding to the corresponding vehicle type identification device. The number of accurate license plate recognition is the number of correct license plates in the vehicle license plates recognized by the system corresponding to the corresponding vehicle type identification device after removing abnormal situations such as no license plate, temporary license plate, blocked license plate, and highlighted license plate. The accurate number of vehicle type recognition may include the accurate number of truck model recognition and / or the accurate number of customer model recognition.

[0092] In an embodiment of the present invention, the identification quantity parameter set of each type of vehicle type identification device can be obtained by analyzing the test flow information of the vehicle type identification device of this type. The test flow information may include the flow information of the corresponding vehicle type identification device for the same test lane area, wherein the test flow information may include the identification quantity of the test vehicle, and may also include one or more combinations of the flow number, flow type, audit status, detection time, license plate identification information, vehicle head identification information and vehicle type identification information of each test vehicle. Each type of vehicle type identification device includes at least one vehicle type identification device.

[0093] In the embodiment of the present invention, optionally, the identification technology used by each type of vehicle type identification device may include one of a vehicle type identification technology based on video, a vehicle type identification technology based on radar and vision fusion, and a vehicle type identification technology based on laser radar. Among them, the vehicle type identification technology based on video may include a technology that uses image processing technology to analyze and process the captured vehicle image to identify the vehicle model, color, license plate and other information; the vehicle type identification technology based on radar and vision fusion may include a technology that uses millimeter wave radar to measure vehicle distance and speed and other information, and then uses video technology to analyze and process the vehicle image to achieve the identification of vehicle model, color, license plate and other information; the vehicle type identification technology based on laser radar may include a technology that uses laser radar to emit a laser beam and receive the reflected beam, and calculates the three-dimensional position and shape of the vehicle by analyzing the reflection time and reflection intensity of the beam, thereby achieving the identification of the vehicle, which is not limited in the embodiment of the present invention.

[0094] 102. Calculate an index set for each type of vehicle type identification device based on the identification quantity parameter set of all vehicle type identification devices.

[0095] In an embodiment of the present invention, the indicator set may include at least one of vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle type recognition accuracy, and equipment log completeness rate.

[0096] The vehicle technical detection accuracy is used to evaluate the traffic counting capability of the corresponding vehicle identification equipment and analyze the perception capability of the corresponding vehicle identification equipment to the gantry traffic. The perception capability of the gantry traffic is the basic capability to ensure the functioning of the identification equipment. Only by accurately perceiving the gantry traffic can the real number of vehicles be fully captured and the captured vehicles be identified.

[0097] The vehicle capture rate is used to indicate the ability of the corresponding vehicle model recognition device to capture the front of the vehicle. If the camera resolution of the vehicle model recognition device is low, the sensor performance is poor, or the algorithm processing capability is insufficient, it may lead to inaccurate capture of the front of the vehicle, thus affecting the accuracy of vehicle model recognition. If the device cannot accurately capture the front of the vehicle, it will cause deviations or errors in vehicle model recognition. Accurately capturing the front of the vehicle means that more feature information of the vehicle can be extracted, such as shape, lines, logos, etc. This feature information is the key basis for vehicle model recognition. If the device cannot accurately capture the front of the vehicle, it will lead to incomplete or incorrect feature extraction, which will affect the accuracy of vehicle model recognition.

[0098] The license plate matching accuracy is used to indicate the license plate matching capability of the corresponding vehicle identification device. License plate matching refers to the manufacturer of the corresponding vehicle identification device splicing at least two of the collected and processed vehicle front pictures, license plate pictures, vehicle side pictures and identification information into one picture. Through license plate matching, the various parts of the vehicle can be spliced ​​into a complete image, so as to more accurately identify the vehicle model, color, license plate number and other information. This splicing technology can greatly improve the accuracy and reliability of vehicle model identification, and also facilitate subsequent data processing and analysis. When performing license plate matching, image processing and computer vision technology are required to accurately detect, locate and splice various parts of the vehicle.

[0099] The license plate recognition accuracy rate is used to indicate the performance of the license plate recognition of the corresponding vehicle model recognition equipment. The license plate number is the unique identifier of the vehicle, so accurate recognition of the license plate number is crucial for vehicle model recognition.

[0100] The vehicle type recognition accuracy may include the truck vehicle type recognition accuracy and / or the bus vehicle type recognition accuracy, and the vehicle type recognition accuracy is used to indicate the recognition capability of the corresponding vehicle type recognition device for vehicles of different toll models, and is the most important indicator for evaluating device performance.

[0101] The device log completeness rate is used to indicate the log completeness of the corresponding vehicle identification device. The log completeness can be used to verify whether the system corresponding to the corresponding vehicle identification device can fully record the expected events or operations (such as application behavior, system configuration and status changes, etc.). Log completeness can also be used to check whether the log records are accurate and whether they can provide a complete view of the application or system.

[0102] 103. According to the index set of all vehicle type identification devices, select the target vehicle type identification devices that meet the preset rating conditions from all vehicle type identification devices.

[0103] In the embodiment of the present invention, the number of target vehicle type identification devices may be greater than or equal to 1. For example, assuming that there are vehicle type identification device A, vehicle type identification device B, and vehicle type identification device C, if vehicle type identification device A and vehicle type identification device B both meet the preset rating condition, then vehicle type identification device A and vehicle type identification device B may both be determined as target vehicle type identification devices; if only vehicle type identification device A meets the preset rating condition, then vehicle type identification device A may be determined as the target vehicle type identification device.

[0104] In the embodiment of the present invention, the preset rating condition may include a condition that the index level of the vehicle type identification device is determined to be a condition corresponding to the preset index level, and may also include a condition that the target index value in the index set of the vehicle type identification device is greater than or equal to the preset index value. The target index value is at least one of all the index values ​​included in the index set, which is not limited in the embodiment of the present invention.

[0105] Specifically, the above step 103 of selecting a target vehicle type identification device that meets a preset rating condition from all vehicle type identification devices according to the indicator set of all vehicle type identification devices may include:

[0106] For each type of vehicle identification device, determine whether the target index value in the index set of the vehicle identification device is greater than or equal to the preset index value, and obtain an index determination result;

[0107] When it is determined that the target index value of the type of vehicle identification device is greater than or equal to the preset index value, the type of vehicle identification device is determined as a target vehicle identification device that meets the preset rating condition;

[0108] When it is determined that the target index value of the vehicle type identification device of this type is less than the preset index value, the vehicle type identification device of this type is excluded. The target index value may be one of the vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle type recognition accuracy, and device log completeness rate. And the target index value and the second index value are not the same index value. In this way, the screening accuracy and reliability of the target vehicle type identification device can be improved based on the target index value and the preset rating conditions.

[0109] 104. Determine at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device.

[0110] In the embodiment of the present invention, the location of the deployment point is used to deploy at least one target vehicle type recognition device. The target lane area and the test lane area can be the same lane area or different lane areas, which is not limited in the embodiment of the present invention.

[0111] It can be seen that implementation Figure 1 The described method for deploying vehicle type recognition devices can determine the identification quantity parameter set of each type of vehicle type recognition device among multiple types of vehicle type recognition devices, and calculate the index set of each type of vehicle type recognition device based on the identification quantity parameter set of all vehicle type recognition devices, which can improve the calculation accuracy of the index set of each type of vehicle type recognition device based on the identification quantity parameter set with parameter diversity and can improve the content richness of the index set. Subsequently, based on the accurately calculated index set of all vehicle type recognition devices, the target vehicle type recognition devices that meet the preset rating conditions are screened out from all vehicle type recognition devices, which can improve the screening accuracy of the target vehicle type recognition devices. Then, based on the target identification quantity parameter set of the accurately screened target vehicle type recognition devices, at least one deployment point of the target vehicle type recognition device is determined from the target lane area to be deployed, which can improve the determination accuracy of the deployment point of the target vehicle type recognition device and improve the deployment accuracy of the target vehicle type recognition device, thereby facilitating the reduction of vehicle missed identification / misidentification based on the accurately deployed target vehicle type recognition devices, and further facilitating the reduction of the loss of lane tolls.

[0112] In an optional embodiment, the calculation of the index set of each type of vehicle type identification device according to the identification quantity parameter set of all vehicle type identification devices in step 102 may include:

[0113] Calculate the actual number of vehicles in the test lane area based on the recognition quantity parameter set of all vehicle type recognition devices;

[0114] Calculate the index value corresponding to each identification quantity parameter of each type of vehicle identification device according to the actual number of vehicles, each identification quantity parameter of each type of vehicle identification device, and the index calculation method corresponding to each identification quantity parameter;

[0115] The index values ​​corresponding to all the identification quantity parameters of each type of vehicle type identification device are combined to obtain an index set for each type of vehicle type identification device.

[0116] In the embodiment of the present invention, the actual number of vehicles may include the actual number of trucks and / or the actual number of buses. When the test vehicle includes only trucks, the actual number of vehicles is the actual number of trucks; when the test vehicle includes only buses, the actual number of vehicles is the actual number of buses; when the test vehicle includes both trucks and buses, the actual number of vehicles is the actual number of trucks and the actual number of buses.

[0117] Specifically, the formula for calculating the index corresponding to the vehicle counting detection accuracy is:

[0118]

[0119] The formula for calculating the indicator corresponding to the vehicle capture rate is:

[0120]

[0121] The formula for calculating the indicator corresponding to the correct rate of card type matching is:

[0122]

[0123] The formula for calculating the indicator corresponding to the license plate recognition accuracy is:

[0124]

[0125] The formula for calculating the indicator corresponding to the truck model recognition accuracy is:

[0126]

[0127] The formula for calculating the indicator corresponding to the bus model recognition accuracy is:

[0128]

[0129] It can be seen that this optional embodiment can calculate the actual number of vehicles in the test lane area based on the identification number parameter set of all vehicle type identification devices, which can improve the calculation accuracy of the actual number of vehicles, and then calculate the index value corresponding to the identification number parameter of each type of vehicle type identification device based on the accurately calculated actual number of vehicles, each identification number parameter of each type of vehicle type identification device, and the index calculation method corresponding to the identification number parameter, which can improve the calculation accuracy of the index value, and then combine all the accurately calculated index values ​​in each type of vehicle type identification device to obtain the index set of each type of vehicle type identification device, which can improve the calculation accuracy and reliability of the index set of each type of vehicle type identification device.

[0130] In this optional embodiment, as an optional implementation, the above-mentioned calculation of the actual number of vehicles in the test lane area according to the recognition quantity parameter set of all vehicle type recognition devices may include:

[0131] According to the identification quantity parameter sets of all vehicle type identification devices, a target identification quantity parameter set is selected from all identification quantity parameter sets, where the target identification quantity parameter set includes at least a first identification quantity parameter set and a second identification quantity parameter set;

[0132] Comparing the first identification quantity parameter set and the second identification quantity parameter set to obtain a comparison result;

[0133] According to the comparison result, the first identification quantity parameter set and the second identification quantity parameter set are integrated to obtain an integrated identification quantity parameter set;

[0134] According to the integrated set of identification quantity parameters, the test vehicles in the test lane area are analyzed to obtain the actual number of vehicles in the test lane area.

[0135] In an embodiment of the present invention, optionally, the comparison result may include the same vehicle parameters and different vehicle parameters, wherein the same vehicle parameters are the same parameters of the vehicles represented by the first identification quantity parameter set and the second identification quantity parameter set, and the different vehicle parameters are the different parameters of the vehicles represented by the first identification quantity parameter set and the second identification quantity parameter set. Specifically, the above-mentioned integration of the first identification quantity parameter set and the second identification quantity parameter set according to the comparison result to obtain an integrated identification quantity parameter set may include: merging the first identification quantity parameter set and the second identification quantity parameter set according to the different vehicle parameters included in the comparison result to obtain a merged identification quantity parameter set; and deduplicating the merged identification quantity parameter set according to the same vehicle parameters included in the comparison result to obtain a deduplicated identification quantity parameter set, and determining the deduplicated identification quantity parameter set as the integrated identification quantity parameter set. This can improve the integration accuracy of the identification quantity parameter set, thereby facilitating the improvement of the subsequent analysis accuracy of the actual number of vehicles based on the accurately integrated identification quantity parameter set.

[0136] Specifically, analyzing the test vehicles in the test lane area according to the integrated set of identification number parameters to obtain the actual number of vehicles in the test lane area may include: for any identification number parameter in the integrated set of identification number parameters, counting the test vehicles in the test lane area according to the identification number parameter to obtain the actual number of vehicles in the test lane area. For example: counting the test vehicles according to the integrated accurate number of vehicle model identification to obtain the actual number of vehicles.

[0137] It can be seen that this optional implementation method can select a target identification number parameter set (the target identification number parameter set includes a first identification number parameter set and a second identification number parameter set) from all identification number parameter sets based on the identification number parameter sets of all vehicle type identification devices, and can improve the selection accuracy of the target identification number parameter set, and then compare the first identification number parameter set and the second identification number parameter set to obtain a comparison result, and then integrate the first identification number parameter set and the second identification number parameter set based on the comparison result to obtain an integrated identification number parameter set, and can improve the integration accuracy of the target identification number parameter set based on the first identification number parameter set and the second identification number parameter set, and then analyze the test vehicles in the test lane area based on the accurately integrated identification number parameter set to obtain the actual number of vehicles in the test lane area, and can improve the analysis accuracy and reliability of the actual number of vehicles based on the identification number parameters identified by the vehicle type identification device.

[0138] In this optional implementation, optionally, selecting a target recognition quantity parameter set from all recognition quantity parameter sets according to the recognition quantity parameter sets of all vehicle type recognition devices may include:

[0139] Calculating the recognition quantity parameter sets of all vehicle type recognition devices to obtain a plurality of parameter differences, each parameter difference being used to represent the difference between the recognition quantity parameter sets of any two vehicle type recognition devices;

[0140] A target parameter difference greater than or equal to a preset difference is selected from all parameter differences, and a recognition quantity parameter set of all vehicle type recognition devices corresponding to the target parameter difference is determined as a target recognition quantity parameter set.

[0141] In the embodiment of the present invention, optionally, the preset difference degree may be 80%, or 95%, or any other set value, which is not limited in the embodiment of the present invention.

[0142] It can be seen that this optional implementation method can also calculate the identification quantity parameter sets of all vehicle type identification devices to obtain multiple parameter differences, which can improve the calculation accuracy of the difference between the identification quantity parameter sets of every two vehicle type identification devices, and then select the one with a larger difference from all parameter differences as the target parameter difference, which can improve the selection accuracy of the target parameter difference, and determine the identification quantity parameter set corresponding to the accurately selected target parameter difference as the target identification quantity parameter set, which can improve the selection accuracy of the target identification quantity parameter set.

[0143] Embodiment 2

[0144] See also Figure 2 , Figure 2 1 is a flow chart of a method for deploying a vehicle type identification device disclosed in an embodiment of the present invention. Figure 2 The deployment method of the vehicle type identification device described above can be applied to a deployment system of the vehicle type identification device, wherein the system may include a deployment device or a deployment server, wherein the deployment server may include a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 2 As shown, the deployment method of the vehicle type identification device may include the following operations:

[0145] 201. Determine a set of identification quantity parameters for each type of vehicle type identification device among multiple types of vehicle type identification devices.

[0146] 202. Calculate an index set for each type of vehicle type identification device based on the identification quantity parameter set of all vehicle type identification devices.

[0147] 203. According to the indicator set of all vehicle type identification devices, select the target vehicle type identification device that meets the preset rating conditions from all vehicle type identification devices.

[0148] In the embodiment of the present invention, for other descriptions of step 201 to step 203, please refer to the detailed description of step 101 to step 103 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0149] 204. Analyze the flow of each vehicle type among the multiple vehicle types identified by the target vehicle type identification device according to the target identification quantity parameter set of the target vehicle type identification device.

[0150] 205. Classify all vehicle models according to the preset vehicle model identification method to obtain classification results.

[0151] In an embodiment of the present invention, optionally, the vehicle model identification method may include a first vehicle model identification method and a second vehicle model identification method. Among them, the first vehicle model identification method may include a method of identifying the ETC vehicle model information sent by the ETC device configured for the test vehicle and received by the vehicle model identification device. The second vehicle model identification method may include a method of obtaining the relevant image of the test vehicle at the entrance of the test lane area through the vehicle model identification device and identifying the vehicle model of the image. The classification result may include a first-category vehicle model set and a second-category vehicle model set, wherein the first-category vehicle model set is empty or all first-category vehicle models in the first-category vehicle model set are identified by the first-category vehicle model identification method, the second-category vehicle model set is empty or all second-category vehicle models in the second-category vehicle model set are identified by the second-category vehicle model identification method, and each second-category vehicle model has a corresponding identification location.

[0152] 206. According to the flow rates of all the second-category vehicle types included in the classification results, the identification locations corresponding to all the second-category vehicle types are screened to obtain at least one target identification location.

[0153] In an embodiment of the present invention, specifically, screening the identification locations corresponding to all second-category models according to the flow rates of all second-category models included in the classification results to obtain at least one target identification location may include: calculating the proportion of the flow rate of each second-category model in the flow rates of all second-category models according to the flow rates of all second-category models included in the classification results as the flow rate proportion corresponding to each second-category model, and selecting at least one of the second-category models whose corresponding flow rate proportion is greater than or equal to the preset flow rate proportion from all second-category models as the target model, and determining the identification location corresponding to the target model as the target identification location. In this way, the accuracy and reliability of screening the target identification location can be improved based on the processing of the flow rate proportion corresponding to the second-category model.

[0154] 207. Determine the target identification location as a deployment point for the target vehicle type identification device.

[0155] It can be seen that implementation Figure 2The described method for deploying vehicle type recognition devices can determine the identification quantity parameter set of each type of vehicle type recognition device among multiple types of vehicle type recognition devices, and calculate the index set of each type of vehicle type recognition device based on the identification quantity parameter set of all vehicle type recognition devices, which can improve the calculation accuracy of the index set of each type of vehicle type recognition device based on the identification quantity parameter set with parameter diversity and can improve the content richness of the index set. Subsequently, based on the accurately calculated index set of all vehicle type recognition devices, the target vehicle type recognition devices that meet the preset rating conditions are screened out from all vehicle type recognition devices, which can improve the screening accuracy of the target vehicle type recognition devices. Then, based on the target identification quantity parameter set of the accurately screened target vehicle type recognition devices, at least one deployment point of the target vehicle type recognition device is determined from the target lane area to be deployed, which can improve the determination accuracy of the deployment point of the target vehicle type recognition device and improve the deployment accuracy of the target vehicle type recognition device, thereby facilitating the reduction of vehicle missed identification / misidentification based on the accurately deployed target vehicle type recognition devices, and further facilitating the reduction of the loss of lane tolls. In addition, it is also possible to analyze the flow of each of the multiple vehicle models identified by the target vehicle model identification device according to the target identification quantity parameter set of the target vehicle model identification device, thereby improving the analysis accuracy of the flow of each vehicle model, and then classify all vehicle models according to the preset vehicle model identification method to obtain classification results (the classification results include a first-category vehicle model set and a second-category vehicle model set), thereby improving the accuracy of classifying all vehicle models based on the vehicle model identification method, and then, based on the flow of the second-category vehicle model, screening out at least one target identification location from the identification locations corresponding to all second-category vehicle models as the deployment point of the target vehicle model identification device, thereby improving the screening accuracy of the target identification location based on the vehicle model flow, thereby improving the accuracy and reliability of determining the deployment point, and facilitating the subsequent deployment of the target vehicle model identification device.

[0156] In an optional embodiment, the method may further include:

[0157] After determining the deployment points in the target lane area, obtaining environmental information of each deployment point and traffic flow information of each deployment point;

[0158] Determine the equipment deployment requirements for each deployment point based on the environmental information of each deployment point and the traffic flow information of each deployment point;

[0159] According to the equipment deployment requirements of each deployment point, formulate an equipment deployment plan that matches each deployment point.

[0160] In an embodiment of the present invention, the environmental information of each deployment point may include the spatial information of each deployment point and / or the weather information of each deployment point. The spatial information of each deployment point may include the number of obstacles at each deployment point, the position of the corresponding obstacle, the type of the corresponding obstacle, the shape of the corresponding obstacle, and the volume of the corresponding obstacle. The weather information of each deployment point may include one or more combinations of air temperature, air humidity, air index, and sunshine intensity at each deployment point in each of multiple time periods.

[0161] It should be noted that when the target lane area and the test lane area are the same lane area, the traffic flow information of each deployment point may include the flow of the second type of vehicle in the identification location corresponding to the deployment point.

[0162] In the embodiment of the present invention, the equipment deployment requirements include the deployment quantity requirements of the target vehicle identification equipment in the corresponding deployment point and the deployment association relationship between each target vehicle identification equipment. Among them, the deployment association relationship between each target vehicle identification equipment may include the association relationship of the equipment distance required between each target vehicle identification equipment, and may also include the association relationship of the identification distance required between each target vehicle identification equipment. Among them, the equipment distance required between each target vehicle identification equipment may be determined by the distance between each target vehicle identification equipment that is interfered with (such as: the equipment distance may be the minimum distance between each target vehicle identification equipment that will not be interfered with), and the identification distance required between each target vehicle identification equipment may be determined by the overlapping range in the identification range between each target vehicle identification equipment. Specifically, in the process of deploying the target vehicle identification equipment, in order to reduce the interference between the target vehicle identification equipment, the distance between the target vehicle identification equipment should not be too close; at the same time, in order to achieve comprehensive identification of the passing vehicles, the distance between the target vehicle identification equipment should not be too far, which is not limited in the embodiment of the present invention.

[0163] It can be seen that this optional embodiment can obtain the environmental information and vehicle flow information of each deployment point after determining the deployment point, and determine the equipment deployment requirements of each deployment point based on the environmental information and vehicle flow information of each deployment point, which can improve the accuracy of determining the equipment deployment requirements, and then formulate an equipment deployment plan that matches each deployment point based on the accurately determined equipment deployment requirements, which can improve the accuracy and reliability of formulating the equipment deployment plan based on the equipment deployment requirements, thereby facilitating improving the deployment accuracy of vehicle model identification equipment based on the equipment deployment plan, and further facilitating reducing the subsequent occurrence of missed / misidentified vehicles, and reducing the loss of lane tolls.

[0164] In another optional embodiment, the above step 203, based on the index set of all vehicle type identification devices, screening out the target vehicle type identification device that meets the preset rating condition from all vehicle type identification devices may include:

[0165] According to the index set of each type of vehicle identification equipment, each type of vehicle identification equipment is comprehensively rated to obtain the rating result of each type of vehicle identification equipment;

[0166] According to the rating results of all vehicle type recognition devices, a target vehicle type recognition device that meets preset rating conditions is determined from all vehicle type recognition devices.

[0167] In the embodiment of the present invention, the rating result is used to indicate the index level of the corresponding vehicle type identification device. The preset rating condition includes a condition that the index level is corresponding to the preset index level.

[0168] It can be seen that this optional embodiment can comprehensively rate each type of vehicle type identification device according to the indicator set of each type of vehicle type identification device, and obtain corresponding rating results (the rating results include indicator levels), which can improve the rating accuracy of the indicator levels of vehicle type identification devices, and according to the rating results obtained by accurate rating, determine the target vehicle type identification device that meets the preset rating conditions from all vehicle type identification devices, and improve the determination accuracy of the target vehicle type identification device based on the rating results.

[0169] In this optional embodiment, as an optional implementation, the above-mentioned determining a target vehicle type recognition device that meets a preset rating condition from all vehicle type recognition devices according to the rating results of all vehicle type recognition devices includes:

[0170] For each type of vehicle identification device, determine whether the index level represented by the rating result of the vehicle identification device of this type is a preset index level;

[0171] If it is determined that the index level of the vehicle type identification device of this type is not the preset index level, then the vehicle type identification device of this type is excluded;

[0172] If it is determined that the index level of the type of vehicle identification device is the preset index level, the type of vehicle identification device is determined to be the target vehicle identification device.

[0173] In the embodiment of the present invention, the index level can be optionally divided into a first index level, a second index level and a third index level. The first index level indicates that the performance of the vehicle type identification device at this level is better, the second index level indicates that the performance of the vehicle type identification device at this level is average, and the third index level indicates that the performance of the vehicle type identification device at this level is poor. Optionally, the preset index level can be the first index level or the second index level, which is not limited in the embodiment of the present invention.

[0174] It can be seen that this optional implementation method can determine whether the indicator level represented by the rating result of each type of vehicle model identification device is the preset indicator level; if not, this type of vehicle model identification device is excluded; if so, this type of vehicle model identification device is determined as the target vehicle model identification device, thereby improving the determination accuracy and reliability of the target vehicle model identification device.

[0175] Embodiment 3

[0176] See also Figure 3 , Figure 3 Schematic diagram of a deployment system of a vehicle type identification device disclosed in an embodiment of the present invention. Figure 3 The deployment system of the vehicle type identification device described may include a deployment device or a deployment server, wherein the deployment server may include a cloud server or a local server, which is not limited in the embodiment of the present invention. Figure 3 As shown, the deployment system of the vehicle type identification device may include:

[0177] The determination module 301 is used to determine the identification quantity parameter set of each type of vehicle type identification device among multiple types of vehicle type identification devices. The identification quantity parameter set includes one or more identification quantity parameters, and each identification quantity parameter includes one of the number of identified vehicles, the number of vehicle head captures, the number of correct license plate matches, the number of correct license plate recognitions, and the number of accurate vehicle type recognitions.

[0178] The calculation module 302 is used to calculate an indicator set for each type of vehicle type identification device based on the identification quantity parameter set of all vehicle type identification devices, and the indicator set includes at least one of vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle type recognition accuracy and equipment log completeness rate.

[0179] The screening module 303 is used to screen out target vehicle type identification devices that meet preset rating conditions from all vehicle type identification devices according to the indicator set of all vehicle type identification devices.

[0180] The determination module 301 is also used to determine at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device, and the location of the deployment point is used to deploy at least one target vehicle type recognition device.

[0181] It can be seen that implementation Figure 3The described vehicle type identification device deployment system can determine the identification quantity parameter set of each type of vehicle type identification device among multiple types of vehicle type identification devices, and calculate the index set of each type of vehicle type identification device based on the identification quantity parameter set of all vehicle type identification devices, which can improve the calculation accuracy of the index set of each type of vehicle type identification device based on the identification quantity parameter set with parameter diversity and improve the content richness of the index set. Subsequently, based on the accurately calculated index set of all vehicle type identification devices, the target vehicle type identification device that meets the preset rating conditions is screened out from all vehicle type identification devices, which can improve the screening accuracy of the target vehicle type identification device. Then, based on the target identification quantity parameter set of the accurately screened target vehicle type identification device, at least one deployment point of the target vehicle type identification device is determined from the target lane area to be deployed, which can improve the determination accuracy of the deployment point of the target vehicle type identification device and improve the deployment accuracy of the target vehicle type identification device, thereby facilitating the reduction of vehicle missed identification / misidentification based on the accurately deployed target vehicle type identification device, and further facilitating the reduction of lane toll loss.

[0182] In an optional embodiment, the identification quantity parameter set of each type of vehicle identification device is obtained by analyzing the test flow information of the vehicle identification device of this type, and the test flow information includes the flow information of the corresponding vehicle identification device for the same test lane area, and the test flow information includes the identification quantity of the test vehicle, and also includes the flow number, flow type, audit status, detection time, license plate recognition information, vehicle head recognition information and vehicle type recognition information of each test vehicle. One or more combinations, each type of vehicle identification device includes at least one vehicle identification device. And, the calculation module 302 calculates the index set of each type of vehicle identification device according to the identification quantity parameter set of all vehicle identification devices, which may specifically include:

[0183] Calculate the actual number of vehicles in the test lane area based on the recognition quantity parameter set of all vehicle type recognition devices;

[0184] Calculate the index value corresponding to each identification quantity parameter of each type of vehicle identification device according to the actual number of vehicles, each identification quantity parameter of each type of vehicle identification device, and the index calculation method corresponding to each identification quantity parameter;

[0185] The index values ​​corresponding to all the identification quantity parameters of each type of vehicle type identification device are combined to obtain an index set for each type of vehicle type identification device.

[0186] It can be seen that this optional embodiment can calculate the actual number of vehicles in the test lane area based on the identification number parameter set of all vehicle type identification devices, which can improve the calculation accuracy of the actual number of vehicles, and then calculate the index value corresponding to the identification number parameter of each type of vehicle type identification device based on the accurately calculated actual number of vehicles, each identification number parameter of each type of vehicle type identification device, and the index calculation method corresponding to the identification number parameter, which can improve the calculation accuracy of the index value, and then combine all the accurately calculated index values ​​in each type of vehicle type identification device to obtain the index set of each type of vehicle type identification device, which can improve the calculation accuracy and reliability of the index set of each type of vehicle type identification device.

[0187] In this optional embodiment, as an optional implementation, the calculation module 302 calculates the actual number of vehicles in the test lane area according to the identification number parameter set of all vehicle type identification devices, which may specifically include:

[0188] According to the identification quantity parameter sets of all vehicle type identification devices, a target identification quantity parameter set is selected from all identification quantity parameter sets, where the target identification quantity parameter set includes at least a first identification quantity parameter set and a second identification quantity parameter set;

[0189] Comparing the first identification quantity parameter set and the second identification quantity parameter set to obtain a comparison result;

[0190] According to the comparison result, the first identification quantity parameter set and the second identification quantity parameter set are integrated to obtain an integrated identification quantity parameter set;

[0191] According to the integrated set of identification quantity parameters, the test vehicles in the test lane area are analyzed to obtain the actual number of vehicles in the test lane area.

[0192] It can be seen that this optional implementation method can select a target identification number parameter set (the target identification number parameter set includes a first identification number parameter set and a second identification number parameter set) from all identification number parameter sets based on the identification number parameter sets of all vehicle type identification devices, and can improve the selection accuracy of the target identification number parameter set, and then compare the first identification number parameter set and the second identification number parameter set to obtain a comparison result, and then integrate the first identification number parameter set and the second identification number parameter set based on the comparison result to obtain an integrated identification number parameter set, and can improve the integration accuracy of the target identification number parameter set based on the first identification number parameter set and the second identification number parameter set, and then analyze the test vehicles in the test lane area based on the accurately integrated identification number parameter set to obtain the actual number of vehicles in the test lane area, and can improve the analysis accuracy and reliability of the actual number of vehicles based on the identification number parameters identified by the vehicle type identification device.

[0193] In this optional implementation, optionally, the calculation module 302 selects the target recognition quantity parameter set from all recognition quantity parameter sets according to the recognition quantity parameter sets of all vehicle type recognition devices, and the method may specifically include:

[0194] Calculating the recognition quantity parameter sets of all vehicle type recognition devices to obtain a plurality of parameter differences, each parameter difference being used to represent the difference between the recognition quantity parameter sets of any two vehicle type recognition devices;

[0195] A target parameter difference greater than or equal to a preset difference is selected from all parameter differences, and a recognition quantity parameter set of all vehicle type recognition devices corresponding to the target parameter difference is determined as a target recognition quantity parameter set.

[0196] It can be seen that this optional implementation method can also calculate the identification quantity parameter sets of all vehicle type identification devices to obtain multiple parameter differences, which can improve the calculation accuracy of the difference between the identification quantity parameter sets of every two vehicle type identification devices, and then select the one with a larger difference from all parameter differences as the target parameter difference, which can improve the selection accuracy of the target parameter difference, and determine the identification quantity parameter set corresponding to the accurately selected target parameter difference as the target identification quantity parameter set, which can improve the selection accuracy of the target identification quantity parameter set.

[0197] In another optional embodiment, the determination module 301 determines at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device, which may specifically include:

[0198] Analyzing the flow of each of the multiple vehicle types identified by the target vehicle type identification device according to the target identification quantity parameter set of the target vehicle type identification device;

[0199] According to the preset vehicle type identification method, all vehicle types are classified to obtain classification results, the vehicle type identification methods include the first vehicle type identification method and the second vehicle type identification method, the classification results include the first type vehicle type set and the second type vehicle type set, the first type vehicle type set is empty or all the first type vehicles in the first type vehicle type set are identified by the first type vehicle type identification method, the second type vehicle type set is empty or all the second type vehicles in the second type vehicle type set are identified by the second type vehicle type identification method, and each second type vehicle type has a corresponding identification location;

[0200] According to the traffic of all second-category vehicles included in the classification results, the identification locations corresponding to all second-category vehicles are screened to obtain at least one target identification location, and the target identification location is determined as the deployment point of the target vehicle model identification device.

[0201] It can be seen that this optional embodiment can analyze the flow of each model among the multiple models identified by the target model identification device according to the target identification quantity parameter set of the target model identification device, and can improve the analysis accuracy of the flow of each model. Subsequently, according to the preset model identification method, all model types are classified to obtain classification results (the classification results include a first category model set and a second category model set). Based on the model identification method, the accuracy of classifying all model types can be improved, and then according to the flow of the second category model, at least one target identification location is screened out from the identification locations corresponding to all second category models as the deployment point of the target model identification device. Based on the model flow, the screening accuracy of the target identification location can be improved, thereby improving the determination accuracy and reliability of the deployment point, which is helpful for the subsequent deployment of the target model identification device.

[0202] In another optional embodiment, Figure 4 As shown, Figure 4 FIG. 1 is a schematic diagram of a deployment system of another vehicle type identification device disclosed in an embodiment of the present invention. Figure 4 As shown, the system may also include an acquisition module 304 and a formulation module 305, wherein:

[0203] The acquisition module 304 is used to acquire the environment information of each deployment point and the traffic flow information of each deployment point after the determination module 301 determines the deployment points in the target lane area.

[0204] The determination module 301 is also used to determine the equipment deployment requirements of each deployment point based on the environmental information of each deployment point and the traffic flow information of each deployment point. The equipment deployment requirements include the deployment quantity requirements of the target vehicle model identification devices in the corresponding deployment point and the deployment association relationship between each target vehicle model identification device.

[0205] The formulation module 305 is used to formulate a device deployment plan matching each deployment point according to the device deployment requirements of each deployment point.

[0206] It can be seen that this optional embodiment can obtain the environmental information and vehicle flow information of each deployment point after determining the deployment point, and determine the equipment deployment requirements of each deployment point based on the environmental information and vehicle flow information of each deployment point, which can improve the accuracy of determining the equipment deployment requirements, and then formulate an equipment deployment plan that matches each deployment point based on the accurately determined equipment deployment requirements, which can improve the accuracy and reliability of formulating the equipment deployment plan based on the equipment deployment requirements, thereby facilitating improving the deployment accuracy of vehicle model identification equipment based on the equipment deployment plan, and further facilitating reducing the subsequent occurrence of missed / misidentified vehicles, and reducing the loss of lane tolls.

[0207] In another optional embodiment, the screening module 303 screens out the target vehicle type identification device that meets the preset rating condition from all vehicle type identification devices according to the index set of all vehicle type identification devices, which may specifically include:

[0208] According to the index set of each type of vehicle identification equipment, each type of vehicle identification equipment is comprehensively rated to obtain the rating result of each type of vehicle identification equipment, and the rating result is used to represent the index level of the corresponding vehicle identification equipment;

[0209] According to the rating results of all vehicle type recognition devices, a target vehicle type recognition device that meets preset rating conditions is determined from all vehicle type recognition devices, and the preset rating conditions include a condition that the index level corresponds to the preset index level.

[0210] It can be seen that this optional embodiment can comprehensively rate each type of vehicle type identification device according to the indicator set of each type of vehicle type identification device, and obtain corresponding rating results (the rating results include indicator levels), which can improve the rating accuracy of the indicator levels of vehicle type identification devices, and according to the rating results obtained by accurate rating, determine the target vehicle type identification device that meets the preset rating conditions from all vehicle type identification devices, and improve the determination accuracy of the target vehicle type identification device based on the rating results.

[0211] In this optional embodiment, as an optional implementation, the screening module 303 determines the target vehicle type identification device that meets the preset rating condition from all vehicle type identification devices according to the rating results of all vehicle type identification devices, which may specifically include:

[0212] For each type of vehicle identification device, determine whether the index level represented by the rating result of the vehicle identification device of this type is a preset index level;

[0213] If it is determined that the index level of the vehicle type identification device of this type is not the preset index level, then the vehicle type identification device of this type is excluded;

[0214] If it is determined that the index level of the type of vehicle identification device is the preset index level, the type of vehicle identification device is determined to be the target vehicle identification device.

[0215] It can be seen that this optional implementation method can determine whether the indicator level represented by the rating result of each type of vehicle model identification device is the preset indicator level; if not, this type of vehicle model identification device is excluded; if so, this type of vehicle model identification device is determined as the target vehicle model identification device, thereby improving the determination accuracy and reliability of the target vehicle model identification device.

[0216] Embodiment 4

[0217] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of a deployment system of another vehicle type identification device disclosed in an embodiment of the present invention. Figure 5 As shown, the deployment system of the vehicle type identification device may include:

[0218] A memory 401 storing executable program codes;

[0219] a processor 402 coupled to the memory 401;

[0220] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the deployment method of the vehicle type identification device described in the first embodiment of the present invention or the second embodiment of the present invention.

[0221] Embodiment 5

[0222] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the deployment method of the vehicle type identification device described in Embodiment 1 or Embodiment 2 of the present invention.

[0223] Embodiment 6

[0224] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the deployment method of the vehicle model identification device described in Embodiment 1 or Embodiment 2.

[0225] The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without paying creative labor.

[0226] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, a magnetic disk storage, a magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0227] Finally, it should be noted that the deployment method and system of a vehicle type identification device disclosed in the embodiment of the present invention discloses only the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions recorded in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deploying vehicle type identification equipment, characterized in that: The method comprises: Determine a set of identification quantity parameters for each type of vehicle identification device among multiple types of vehicle identification devices, the set of identification quantity parameters includes one or more identification quantity parameters, and each of the identification quantity parameters includes one of the number of identified vehicles, the number of vehicle head captures, the number of correct plate type matches, the number of correct license plate recognitions, and the number of accurate vehicle model recognitions. The set of identification quantity parameters for each type of vehicle identification device is obtained by analyzing the test flow information of the type of vehicle identification device, the test flow information includes the flow information of the corresponding vehicle identification device for the same test lane area, and the test flow information includes the identification number of the test vehicle, and also includes one or more combinations of the flow number, flow type, audit status, detection time, license plate recognition information, head recognition information, and vehicle model recognition information of each test vehicle. Each type of vehicle identification device includes at least one vehicle model identification device; Calculate an index set for each type of vehicle identification device according to the identification quantity parameter set of all the vehicle identification devices, wherein the index set includes at least one of vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle model recognition accuracy, and device log completeness rate; According to the indicator set of all the vehicle type identification devices, a target vehicle type identification device that meets a preset rating condition is selected from all the vehicle type identification devices; According to the target recognition quantity parameter set of the target vehicle type recognition device, at least one deployment point of the target vehicle type recognition device is determined from the target lane area to be deployed, and the location of the deployment point is used to deploy at least one target vehicle type recognition device; The step of calculating the index set of each type of vehicle type identification device according to the identification quantity parameter set of all the vehicle type identification devices includes: Calculating the actual number of vehicles in the test lane area according to the identification quantity parameter set of all the vehicle type identification devices; Calculate the index value corresponding to each identification quantity parameter of each type of vehicle identification device according to the actual number of vehicles, each identification quantity parameter of each type of vehicle identification device, and the index calculation method corresponding to each identification quantity parameter; Combining the index values ​​corresponding to all the identification quantity parameters of each type of vehicle type identification device to obtain an index set for each type of vehicle type identification device; The step of calculating the actual number of vehicles in the test lane area according to the identification number parameter set of all the vehicle type identification devices includes: According to the identification quantity parameter sets of all the vehicle type identification devices, selecting a target identification quantity parameter set from all the identification quantity parameter sets, wherein the target identification quantity parameter set includes at least a first identification quantity parameter set and a second identification quantity parameter set; Comparing the first identification quantity parameter set and the second identification quantity parameter set to obtain a comparison result; According to the comparison result, the first identification quantity parameter set and the second identification quantity parameter set are integrated to obtain an integrated identification quantity parameter set; Analyzing the test vehicles in the test lane area according to the integrated set of identification quantity parameters to obtain the actual number of vehicles in the test lane area; The step of selecting a target recognition quantity parameter set from all the recognition quantity parameter sets according to the recognition quantity parameter sets of all the vehicle type recognition devices comprises: Calculating the identification quantity parameter sets of all the vehicle type identification devices to obtain a plurality of parameter differences, each of which is used to represent the difference between the identification quantity parameter sets of any two of the vehicle type identification devices; A target parameter difference greater than or equal to a preset difference is selected from all the parameter differences, and a recognition quantity parameter set of all vehicle type recognition devices corresponding to the target parameter difference is determined as a target recognition quantity parameter set.

2. The method for deploying vehicle type identification equipment according to claim 1, characterized in that: The step of determining at least one deployment point of the target vehicle type recognition device in a target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device comprises: Analyzing the flow of each of the plurality of vehicle types identified by the target vehicle type identification device according to the target identification quantity parameter set of the target vehicle type identification device; According to a preset vehicle type identification method, all the vehicle types are classified to obtain a classification result, wherein the vehicle type identification method includes a first vehicle type identification method and a second vehicle type identification method, and the classification result includes a first type vehicle type set and a second type vehicle type set, wherein the first type vehicle type set is empty or all first type vehicles in the first type vehicle type set are identified by the first type vehicle type identification method, the second type vehicle type set is empty or all second type vehicles in the second type vehicle type set are identified by the second type vehicle type identification method, and each second type vehicle type has a corresponding identification location; According to the traffic of all the second-category vehicles included in the classification result, the identification locations corresponding to all the second-category vehicles are screened to obtain at least one target identification location, and the target identification location is determined as the deployment point of the target vehicle model identification device.

3. The method for deploying vehicle type identification equipment according to claim 2, characterized in that: The method further comprises: After determining the deployment points in the target lane area, obtaining environmental information of each deployment point and traffic flow information of each deployment point; Determine the equipment deployment requirements of each deployment point according to the environmental information of each deployment point and the traffic flow information of each deployment point, wherein the equipment deployment requirements include the deployment quantity requirements of the target vehicle type identification devices in the corresponding deployment point and the deployment association relationship between each target vehicle type identification device; According to the equipment deployment requirements of each of the deployment points, formulate an equipment deployment plan that matches each of the deployment points.

4. The method for deploying vehicle type identification equipment according to any one of claims 1 to 3, characterized in that: The step of selecting a target vehicle type identification device that meets a preset rating condition from all the vehicle type identification devices according to the indicator set of all the vehicle type identification devices includes: According to the index set of each type of vehicle identification device, each type of vehicle identification device is comprehensively rated to obtain a rating result of each type of vehicle identification device, wherein the rating result is used to represent the index level of the corresponding vehicle identification device; According to the rating results of all the vehicle type recognition devices, a target vehicle type recognition device that meets a preset rating condition is determined from all the vehicle type recognition devices, wherein the preset rating condition includes a condition that the indicator level corresponds to a preset indicator level; Wherein, the step of determining a target vehicle type identification device that meets a preset rating condition from all the vehicle type identification devices according to the rating results of all the vehicle type identification devices comprises: For each type of vehicle type identification device, determining whether the index level represented by the rating result of the vehicle type identification device of this type is a preset index level; If it is determined that the index level of the vehicle type identification device of this type is not the preset index level, then the vehicle type identification device of this type is excluded; If it is determined that the index level of the type of vehicle identification device is the preset index level, the type of vehicle identification device is determined to be the target vehicle identification device.

5. A deployment system for vehicle type recognition equipment, characterized in that: The system comprises a determination module, a calculation module and a screening module, wherein: The determination module is used to determine the identification quantity parameter set of each type of vehicle identification device in multiple types of vehicle identification devices, the identification quantity parameter set includes one or more identification quantity parameters, and each of the identification quantity parameters includes one of the number of identified vehicles, the number of vehicle head captured, the number of correct plate type matches, the number of correct license plate recognition, and the number of accurate vehicle model recognition. The identification quantity parameter set of each type of vehicle identification device is obtained by analyzing the test flow information of the type of vehicle identification device of this type, the test flow information includes the flow information of the corresponding vehicle type identification device for the same test lane area, and the test flow information includes the identification number of the test vehicle, and also includes one or more combinations of the flow number, flow type, audit status, detection time, license plate recognition information, head recognition information and vehicle model recognition information of each test vehicle, and each type of vehicle identification device includes at least one vehicle model recognition device; The calculation module is used to calculate an index set of each type of vehicle identification device according to the identification quantity parameter set of all the vehicle identification devices, and the index set includes at least one of vehicle counting detection accuracy, vehicle capture rate, license plate type matching accuracy, license plate recognition accuracy, vehicle model recognition accuracy and equipment log completeness rate; The screening module is used to screen out target vehicle type identification devices that meet preset rating conditions from all the vehicle type identification devices according to the indicator set of all the vehicle type identification devices; The determination module is further used to determine at least one deployment point of the target vehicle type recognition device from the target lane area to be deployed according to the target recognition quantity parameter set of the target vehicle type recognition device, and the location of the deployment point is used to deploy at least one target vehicle type recognition device; The calculation module calculates the index set of each type of vehicle identification device according to the identification quantity parameter set of all vehicle identification devices, specifically including: Calculating the actual number of vehicles in the test lane area according to the identification quantity parameter set of all the vehicle type identification devices; Calculate the index value corresponding to each identification quantity parameter of each type of vehicle identification device according to the actual number of vehicles, each identification quantity parameter of each type of vehicle identification device, and the index calculation method corresponding to each identification quantity parameter; Combining the index values ​​corresponding to all the identification quantity parameters of each type of vehicle type identification device to obtain an index set for each type of vehicle type identification device; The method in which the calculation module calculates the actual number of vehicles in the test lane area according to the identification number parameter set of all the vehicle type identification devices specifically includes: According to the identification quantity parameter sets of all the vehicle type identification devices, selecting a target identification quantity parameter set from all the identification quantity parameter sets, wherein the target identification quantity parameter set includes at least a first identification quantity parameter set and a second identification quantity parameter set; Comparing the first identification quantity parameter set and the second identification quantity parameter set to obtain a comparison result; According to the comparison result, the first identification quantity parameter set and the second identification quantity parameter set are integrated to obtain an integrated identification quantity parameter set; Analyzing the test vehicles in the test lane area according to the integrated set of identification quantity parameters to obtain the actual number of vehicles in the test lane area; The method in which the calculation module selects a target recognition quantity parameter set from all the recognition quantity parameter sets according to the recognition quantity parameter sets of all the vehicle type recognition devices specifically includes: Calculating the identification quantity parameter sets of all the vehicle type identification devices to obtain a plurality of parameter differences, each of which is used to represent the difference between the identification quantity parameter sets of any two of the vehicle type identification devices; A target parameter difference greater than or equal to a preset difference is selected from all the parameter differences, and a recognition quantity parameter set of all vehicle type recognition devices corresponding to the target parameter difference is determined as a target recognition quantity parameter set.

6. A deployment system for vehicle type recognition equipment, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the deployment method of the vehicle model identification device as described in any one of claims 1-4.

7. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the deployment method of the vehicle model identification device as described in any one of claims 1-4.

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