Vehicle barrier gate license plate automatic identification control system

Abstract

The invention relates to a vehicle barrier gate license plate automatic identification control system, and relates to the technical field of image data processing, and the system comprises a vehicle sensing module which is used for sensing whether there is a vehicle in a lane; the vehicle recognition module is used for collecting vehicle infrared sensing information when sensing that a vehicle exists in a lane, and predicting a license plate three-dimensional coordinate of the vehicle to be recognized based on the vehicle infrared sensing information; the image acquisition module is used for judging whether image acquisition pose adjustment is carried out or not based on the predicted license plate three-dimensional coordinates of the to-be-recognized vehicle, if yes, the image of the to-be-recognized vehicle is acquired after the pose of an image acquisition device is adjusted based on the license plate three-dimensional coordinates of the to-be-recognized vehicle, and if not, the image of the to-be-recognized vehicle is acquired; and the license plate recognition module is used for recognizing license plate information of the to-be-recognized vehicle based on the image of the to-be-recognized vehicle, and has the advantages that the intelligent level of automatic license plate recognition of the vehicle barrier gate is improved, and the accuracy of license plate recognition and the passing efficiency of the vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a vehicle barrier automatic license plate recognition control system. Background Art

[0002] The license plate recognition system is an application of computer video image recognition technology in vehicle license plate recognition. License plate recognition is widely used in highway vehicle management. In the electronic toll collection system, it is also the main means of identifying vehicle identity in combination with DSRC technology. License plate recognition technology is combined with the electronic non-stop toll collection system to identify vehicles. Passing vehicles do not need to stop when passing through the road, that is, they can realize automatic vehicle identity recognition and automatic toll collection. In parking lot management, in order to improve the efficiency of vehicle traffic at the entrance and exit, license plate recognition is used for vehicles that do not need to pay parking fees (such as monthly trucks and internal free-pass vehicles). Unmanned fast lanes are built, and the card-free and non-stop entry and exit experience is changing the management mode of entering and exiting parking lots. The gate and license plate recognition system are integrated together and called an all-in-one machine. It is a channel entrance and exit management device specifically used for restricting the driving of motor vehicles on the road. It is mainly used for highway toll stations and parking lot systems to manage vehicle channels and manage vehicle entry and exit. It occupies a small ground area and has relatively low cost and is widely used.

[0003] After the license plate recognition system recognizes the license plate, the control barrier opens and the motor vehicle is allowed to enter. In this process, the license plate recognition technology is very important. Its role is to use image recognition technology to analyze the captured image through software to find the license plate number, and extract it as the vehicle identification code, so as to carry out management activities such as illegal capture and parking fees. However, due to the variety of motor vehicles, the hanging position of the license plate is also different (such as height), and the driving information of each vehicle at the entrance and exit of the parking lot is also different (such as the position of driving off the main road), and the position of the camera at the entrance and exit of the parking lot is generally fixed and not easy to adjust. Therefore, during the shooting process, for different vehicles, due to angle problems, the license plate number is often not well recognized, which will affect the efficiency of vehicle entry and exit.

[0004] Therefore, it is necessary to provide a vehicle barrier automatic license plate recognition control system to improve the intelligence level of vehicle barrier automatic license plate recognition, improve the accuracy of license plate recognition and vehicle traffic efficiency. Summary of the invention

[0005] The present invention provides a vehicle barrier automatic license plate recognition control system, comprising: a vehicle sensing module, used to sense whether there is a vehicle in a lane; a vehicle recognition module, comprising an infrared sensing unit and a parameter determination unit, wherein the infrared sensing unit comprises a plurality of X-axis infrared sensing devices arranged along the width direction of the railing of the barrier and a plurality of Y-axis infrared sensing devices arranged along the length direction of the barrier, the X-axis infrared sensing device comprises a plurality of X-axis infrared sensors arranged along the length direction of the railing, the Y-axis infrared sensing device comprises a plurality of Y-axis infrared sensors arranged along the vertical direction, and the infrared sensing unit is used to collect vehicle information when the vehicle sensing module senses that there is a vehicle in the lane. Infrared sensing information, the parameter determination unit is used to predict the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle infrared sensing information; the image acquisition module is used to determine whether to adjust the image acquisition posture based on the predicted three-dimensional coordinates of the license plate of the vehicle to be identified. If it is determined to adjust the image acquisition posture, a target image acquisition posture is generated based on the three-dimensional coordinates of the license plate of the vehicle to be identified, and the posture of the image acquisition device is adjusted based on the target image acquisition posture. After the posture is adjusted, the image of the vehicle to be identified is acquired. If it is determined not to adjust the image acquisition posture, the image of the vehicle to be identified is acquired; the license plate recognition module is used to identify the license plate information of the vehicle to be identified based on the image of the vehicle to be identified.

[0006] Furthermore, when the vehicle sensing module senses that there is a vehicle in the lane, the infrared sensing unit collects vehicle infrared sensing information, including: starting each of the Y-axis infrared sensors to collect the Y-axis obstacle distance; starting each of the X-axis infrared sensors to collect the X-axis obstacle distance, wherein the vehicle infrared sensing information includes at least the Y-axis obstacle distance and the X-axis obstacle distance.

[0007] Furthermore, the parameter determination unit predicts the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle infrared sensing information, including: determining the vehicle posture change parameters of the vehicle to be identified at the multiple continuous time points based on the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at multiple continuous time points and the X-axis obstacle distances collected by each of the X-axis infrared sensors at multiple continuous time points; determining the target time point from the multiple continuous time points based on the vehicle posture change parameters of the vehicle to be identified at the multiple continuous time points; determining the vehicle model parameters of the vehicle to be identified based on the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at the target time point and the X-axis obstacle distances collected by each of the X-axis infrared sensors at the target time point, wherein the vehicle model parameters of the vehicle to be identified include at least the maximum width of the vehicle, the maximum length of the vehicle, the maximum height of the vehicle and the chassis height of the vehicle; based on the vehicle model parameters of the vehicle to be identified, predicting the three-dimensional coordinates of the license plate of the vehicle to be identified.

[0008] Furthermore, the parameter determination unit determines the vehicle posture change parameters of the vehicle to be identified at the multiple continuous time points based on the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at the multiple continuous time points and the X-axis obstacle distances collected by each of the X-axis infrared sensors at the multiple continuous time points, including: for each of the time points, based on the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at the time point and the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at the previous time point, calculating the Y-axis change parameter at the time point; for each of the time points, based on the X-axis obstacle distances collected by each of the X-axis infrared sensors at the time point and the X-axis obstacle distances collected by each of the X-axis infrared sensors at the previous time point, calculating the X-axis change parameter at the time point; for each of the time points, weightedly summing the Y-axis change parameter at the time point and the X-axis change parameter at the time point to obtain the vehicle posture change parameter of the vehicle to be identified at the time point.

[0009] Furthermore, the parameter determination unit determines the vehicle model parameters of the vehicle to be identified based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the target time point and the X-axis obstacle distance collected by each of the X-axis infrared sensors at the target time point, including: determining the maximum vehicle length and the maximum vehicle height of the vehicle to be identified based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor; determining the maximum vehicle width of the vehicle to be identified based on the X-axis obstacle distance collected by each of the X-axis infrared sensors at the target time point and the three-dimensional coordinate information of each X-axis infrared sensor; determining the vehicle chassis height of the vehicle to be identified based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor, and the X-axis obstacle distance collected by each of the X-axis infrared sensors at the target time point and the three-dimensional coordinate information of each X-axis infrared sensor.

[0010] Furthermore, the parameter determination unit predicts the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle model parameters of the vehicle to be identified, including: obtaining the vehicle model parameters and relative positions of the license plates of multiple test vehicles, wherein the relative positions of the license plates are used to characterize the relative positions of the center of the license plate of the test vehicle and the center points of the two front wheels of the test vehicle; clustering the multiple test vehicles based on the vehicle model parameters of the multiple test vehicles to determine multiple vehicle clusters; based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the target time point and the three-dimensional coordinate information of each of the Y-axis infrared sensors and the X-axis infrared sensors at the target time point The three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified are determined based on the X-axis obstacle distance collected by the point and the three-dimensional coordinate information of each X-axis infrared sensor; a similar vehicle cluster is determined from the multiple vehicle clusters based on the vehicle model parameters of the vehicle to be identified; a similar test vehicle is determined from the similar vehicle clusters based on the vehicle model parameters of the vehicle to be identified; the three-dimensional coordinates of the license plate of the vehicle to be identified are predicted based on the relative position of the license plate of the similar test vehicle and the three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified, wherein the three-dimensional coordinates of the license plate of the vehicle to be identified include the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified.

[0011] Furthermore, the image acquisition module determines whether to adjust the image acquisition posture based on the predicted license plate position of the vehicle to be identified, including: obtaining multiple first training samples, wherein the first training samples include sample image acquisition postures, images acquired by the image acquisition device under the sample image acquisition postures, and three-dimensional coordinates of multiple markers, and the labels of the first training samples are pixel coordinates of multiple markers; establishing a pixel coordinate prediction model; training the pixel coordinate prediction model through the multiple first training samples; using the trained pixel coordinate prediction model to predict the first pixel coordinates of the four corners of the license plate of the vehicle to be identified based on the current image acquisition posture and the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified; and judging whether to adjust the image acquisition posture based on the first pixel coordinates of the four corners of the license plate of the vehicle to be identified.

[0012] Furthermore, the image acquisition module generates a target image acquisition pose based on the license plate position of the vehicle to be identified, including: obtaining a plurality of second training samples, wherein the second training samples include the sample image acquisition pose before adjustment and the three-dimensional coordinates of a plurality of markers, and the label of the second training sample is the sample image acquisition pose after adjustment; establishing a pose adjustment model; training the pose adjustment model through the plurality of second training samples; and using the trained pose adjustment model to generate the target image acquisition pose based on the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified.

[0013] Furthermore, the license plate recognition module identifies the license plate information of the vehicle to be identified based on the image of the vehicle to be identified, including: predicting the second pixel coordinates of the four corners of the license plate of the vehicle to be identified based on the target image acquisition posture and the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified through the pixel coordinate prediction model; extracting a target area image from the image of the vehicle to be identified based on the predicted second pixel coordinates of the four corners of the license plate of the vehicle to be identified; and identifying the license plate information of the vehicle to be identified based on the target area image.

[0014] Furthermore, the license plate recognition module identifies the license plate information of the vehicle to be identified based on the target area image, including: extracting a background area image and multiple license plate character images based on the target area image; judging whether the license plate of the vehicle to be identified is genuine based on the background area image and the multiple license plate character images; when it is determined that the license plate of the vehicle to be identified is genuine, performing optical character recognition on the multiple license plate character images to determine the license plate number of the vehicle to be identified; and generating a warning message when it is determined that the license plate of the vehicle to be identified is fake.

[0015] Compared with the prior art, the vehicle barrier automatic license plate recognition control system provided in this specification has at least the following beneficial effects: 1. Multiple X-axis infrared sensing devices set along the width direction of the barrier and multiple Y-axis infrared sensing devices set along the length direction of the barrier can accurately collect infrared sensing information of the vehicle, and can more accurately predict the three-dimensional coordinates of the license plate, and then can intelligently determine whether the posture of the image acquisition device needs to be adjusted according to the predicted three-dimensional coordinates of the license plate. If the current posture is not conducive to clearly capturing the license plate image, the system will automatically adjust to the best acquisition posture to ensure that each image collected is of high quality, thereby improving the efficiency and success rate of image acquisition. The system can adapt to vehicles of different sizes, types and driving postures. Due to the use of multi-dimensional infrared sensing and intelligent posture adjustment mechanism, it can maintain a high recognition rate and stability even in the face of complex and changing traffic scenes. Through the automated and intelligent license plate recognition process, manual intervention and waiting time are reduced, and the efficiency of vehicle passage is improved. For car owners, this means shorter waiting time and smoother passage experience. Due to reduced manual intervention and improved recognition efficiency, the system can significantly reduce the operating costs of parking lots or related management agencies. At the same time, high-quality image acquisition and recognition also reduce disputes and losses caused by misidentification or missed recognition; 2. By analyzing the posture change parameters of the vehicle at multiple consecutive time points, the system can more accurately grasp the dynamic driving state of the vehicle. Through the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point, not only the maximum width, length and height of the vehicle are determined, but also the chassis height of the vehicle is determined. This comprehensive vehicle model recognition helps the system to better understand the characteristics of different vehicles and provide a more accurate parameter basis for subsequent license plate recognition. By clustering the vehicle model parameters and license plate relative positions of multiple test vehicles, the system can learn the relationship between different vehicle models and license plate positions. This enables the system to find similar vehicle clusters and similar test vehicles more quickly when facing vehicles of different brands and models, thereby improving the adaptability and robustness of the system. After determining the three-dimensional coordinates of the license plate, the system can perform image acquisition and processing more targeted. This avoids unnecessary image acquisition and processing work, saves image processing resources, and improves the overall efficiency and performance of the system.

[0016] 3. The system can intelligently determine whether the position and posture of the image acquisition device needs to be adjusted based on the predicted position of the license plate of the vehicle to be identified. This dynamic adjustment ensures that the license plate image collected each time is clear and unobstructed, thereby improving the quality of image acquisition. By using the trained pixel coordinate prediction model, the system can quickly predict the pixel coordinates of the license plate in the image, and then determine whether the image acquisition posture needs to be adjusted. This rapid prediction and judgment reduces unnecessary image acquisition and adjustment time and improves the efficiency of image acquisition. The system can adapt to the changes in the position and angle of the license plate of different vehicles. By training the pixel coordinate prediction model and the posture adjustment model, the system can learn the relationship between the pixel coordinates of the license plates of different vehicles and the image acquisition posture, so that it can quickly make adjustments when facing different vehicles to ensure accurate recognition of the license plate. In the existing image acquisition method of the gate, the image acquisition angle is fixed, which may result in incomplete images of the license plate. By introducing the pixel coordinate prediction model and the posture adjustment model, the system can more accurately control the image acquisition process and reduce unnecessary resource consumption.

[0017] 4. The system first predicts the exact position of the license plate in the image through the pixel coordinate prediction model, and then extracts the target area image based on this position. This method reduces the impact of background noise and other interference factors on license plate recognition, thereby improving the accuracy of recognition. Before recognizing the license plate information, the system will also judge the authenticity of the license plate. By extracting the background area image and the license plate character image, the system can analyze the integrity, color, font and other characteristics of the license plate, and effectively identify forged or tampered license plates. This anti-counterfeiting capability is of great significance for maintaining traffic order and public safety. When recognizing the license plate, the system only processes the extracted target area image instead of analyzing the entire vehicle image. This method reduces the amount of data processed, thereby improving the processing speed, allowing the vehicle to pass through the gate quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a module diagram of a vehicle barrier automatic license plate recognition control system shown in one embodiment of the present application; Figure 2 It is a flow chart of predicting the three-dimensional coordinates of the license plate of a vehicle to be identified shown in one embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly introduces the drawings required for describing the embodiments.

[0020] Figure 1 is a module diagram of a vehicle barrier automatic license plate recognition control system shown in an embodiment of the present application, such as Figure 1 As shown, a vehicle barrier automatic license plate recognition control system may include a vehicle sensing module, a vehicle recognition module, an image acquisition module and a license plate recognition module.

[0021] The vehicle sensing module can be used to sense whether there is a vehicle in the lane.

[0022] Specifically, the vehicle sensing module may include a ground sensing coil, which can be installed under the ground of the barrier. When a vehicle passes by, the metal part of the vehicle will induce the ground sensing coil, thereby changing the induced current in the coil. By detecting the change in the induced current, it can be determined whether there is a vehicle in the lane.

[0023] The vehicle identification module may include an infrared sensing unit and a parameter determination unit, wherein the infrared sensing unit includes a plurality of X-axis infrared sensing devices arranged along the width direction of the railing of the barrier and a plurality of Y-axis infrared sensing devices arranged along the length direction of the barrier, the X-axis infrared sensing device includes a plurality of X-axis infrared sensors arranged along the length direction of the railing, and the Y-axis infrared sensing device includes a plurality of Y-axis infrared sensors arranged along the vertical direction, the infrared sensing unit is used to collect vehicle infrared sensing information when the vehicle sensing module senses that there is a vehicle in the lane, and the parameter determination unit is used to predict the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle infrared sensing information.

[0024] Specifically, multiple Y-axis infrared sensing devices are arranged along a straight line parallel to the length direction of the barrier. When a vehicle moves along the barrier, as the vehicle moves, multiple Y-axis infrared sensing devices can detect the presence of the vehicle in sequence. The infrared rays emitted by the X-axis infrared sensor are parallel to the length direction of the barrier, the infrared rays emitted by the Y-axis infrared sensor are perpendicular to the infrared rays emitted by the X-axis infrared sensor, and the infrared rays emitted by any two Y-axis infrared sensors are parallel to each other.

[0025] Furthermore, when the vehicle sensing module senses that there is a vehicle in the lane, the infrared sensing unit collects vehicle infrared sensing information, including: Start each Y-axis infrared sensor to collect the Y-axis obstacle distance, where the Y-axis obstacle distance is used to represent the straight-line distance between the side of the vehicle and the Y-axis infrared sensor; Each X-axis infrared sensor is started to collect the X-axis obstacle distance, wherein the vehicle infrared sensing information includes at least the Y-axis obstacle distance and the X-axis obstacle distance, and the X-axis obstacle distance is used to represent the straight-line distance between the side of the vehicle and the X-axis infrared sensor.

[0026] Furthermore, the parameter determination unit predicts the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle infrared sensing information, including: Determine vehicle posture change parameters of the vehicle to be identified at multiple consecutive time points based on the Y-axis obstacle distances collected by each Y-axis infrared sensor at multiple consecutive time points and the X-axis obstacle distances collected by each X-axis infrared sensor at multiple consecutive time points; Determine a target time point from the multiple continuous time points based on vehicle posture change parameters of the vehicle to be identified at the multiple continuous time points; Determine the vehicle model parameters of the vehicle to be identified based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point, wherein the vehicle model parameters of the vehicle to be identified at least include the maximum width of the vehicle, the maximum length of the vehicle, the maximum height of the vehicle, and the chassis height of the vehicle; Based on the model parameters of the vehicle to be identified, predict the three-dimensional coordinates of the license plate of the vehicle to be identified.

[0027] Further, the parameter determination unit determines the vehicle posture change parameters of the vehicle to be identified at multiple consecutive time points based on the Y-axis obstacle distances collected by each Y-axis infrared sensor at multiple consecutive time points and the X-axis obstacle distances collected by each X-axis infrared sensor at multiple consecutive time points, including: For each time point, based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the time point and the Y-axis obstacle distance collected by each Y-axis infrared sensor at the previous time point, the Y-axis change parameter at the time point is calculated; For each time point, based on the X-axis obstacle distance collected by each X-axis infrared sensor at the time point and the X-axis obstacle distance collected by each X-axis infrared sensor at the previous time point, the X-axis change parameter at the time point is calculated; For each time point, the Y-axis change parameter and the X-axis change parameter of the time point are weighted and summed to obtain the vehicle posture change parameter of the vehicle to be identified at the time point.

[0028] The Y-axis change parameter at a time point can be calculated according to the following formula:

[0029] in, is the Y-axis variation parameter at the t-th time point, is the weight corresponding to the i-th Y-axis infrared sensing device, is greater than 0, and , is the Y-axis change parameter of the i-th Y-axis infrared sensing device at the t-th time point, is the total number of infrared sensing devices on the Y axis, is the Y-axis obstacle distance collected by the j-th Y-axis infrared sensor included in the i-th Y-axis infrared sensing device at the t-th time point, is the Y-axis obstacle distance collected by the j-th Y-axis infrared sensor included in the i-th Y-axis infrared sensing device at the t-1-th time point (i.e., the time point before the t-th time point), is the total number of Y-axis infrared sensors included in the i-th Y-axis infrared sensing device.

[0030] The calculation method of the X-axis change parameter of the time point is similar to the calculation method of the Y-axis change parameter of the time point, which will not be repeated here.

[0031] The vehicle posture change parameters of the vehicle to be identified at a time point can be calculated according to the following formula:

[0032] in, is the vehicle posture change parameter of the vehicle to be identified at the tth time point, and is the weight, and and greater than 0, , is the X-axis variation parameter at the tth time point.

[0033] If there is a time point at which the vehicle posture change parameter is less than the vehicle posture change parameter threshold, and the vehicle posture change parameters at the time points before this time point are all greater than or equal to the vehicle posture change parameter threshold, and the vehicle posture change parameters at the time points after this time point are all less than the vehicle posture change parameter threshold, then this time point is selected as the target time point. It can be understood that at the target time point, the vehicle to be identified is in a state of approximately parking.

[0034] Further, the parameter determination unit determines the vehicle model parameters of the vehicle to be identified based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point, including: Determine the maximum length and maximum height of the vehicle to be identified based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor; Determine the maximum width of the vehicle to be identified based on the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point and the three-dimensional coordinate information of each X-axis infrared sensor; The vehicle chassis height of the vehicle to be identified is determined based on the Y-axis obstacle distance and the three-dimensional coordinate information of each Y-axis infrared sensor collected at the target time point, and the X-axis obstacle distance and the three-dimensional coordinate information of each X-axis infrared sensor collected at the target time point.

[0035] Specifically, for each Y-axis infrared sensing device, if there are Y-axis infrared sensors among the multiple Y-axis infrared sensors included in the Y-axis infrared sensing device that have a number greater than a preset Y-axis infrared sensor number threshold and the Y-axis obstacle distance collected at the target time point is less than the Y-axis obstacle distance threshold, then the Y-axis infrared sensing device is taken as the target Y-axis infrared sensing device.

[0036] For each target Y-axis infrared sensing device, a Y-axis infrared sensor whose Y-axis obstacle distance collected at a target time point is less than a Y-axis obstacle distance threshold among multiple Y-axis infrared sensors included in the target Y-axis infrared sensing device is used as the target Y-axis infrared sensor included in the target Y-axis infrared sensing device. According to the three-dimensional coordinate information of the target Y-axis infrared sensor, the height of the highest target Y-axis infrared sensor can be used as the vehicle height of the vehicle to be identified corresponding to the target Y-axis infrared sensing device.

[0037] The maximum value of the vehicle height of each target Y-axis infrared sensing device corresponding to the vehicle to be identified is taken as the maximum vehicle height of the vehicle to be identified.

[0038] For any two target Y-axis infrared sensing devices, calculate the distance between the target Y-axis infrared sensors at the same height in the two target Y-axis infrared sensing devices. For example, target Y-axis infrared sensing device A1 and target Y-axis infrared sensing device A2, target Y-axis infrared sensing device A1 includes target Y-axis infrared sensor B1, and target Y-axis infrared sensing device A2 includes target Y-axis infrared sensor B2 at the same height as target Y-axis infrared sensor B1, then calculate the distance between target Y-axis infrared sensor B1 and target Y-axis infrared sensor B2. Take the maximum value of the distance between the two target Y-axis infrared sensors at the same height as the maximum vehicle length of the vehicle to be identified.

[0039] For each X-axis infrared sensing device, if there are X-axis infrared sensors greater than a preset X-axis infrared sensor number threshold among the multiple X-axis infrared sensors included in the X-axis infrared sensing device, and the X-axis obstacle distance collected at the target time point is less than the X-axis obstacle distance threshold, then the X-axis infrared sensing device is used as the target X-axis infrared sensing device.

[0040] For each target X-axis infrared sensing device, the X-axis infrared sensor whose X-axis obstacle distance collected at the target time point is less than the Y-axis obstacle distance threshold value among the multiple X-axis infrared sensors included in the target X-axis infrared sensing device is used as the target X-axis infrared sensor included in the target X-axis infrared sensing device. According to the three-dimensional coordinate information of the target X-axis infrared sensor, the straight-line distance between the two target X-axis infrared sensors located at both ends can be calculated as the vehicle width of the vehicle to be identified corresponding to the target X-axis infrared sensing device.

[0041] The maximum value of the vehicle width of each target X-axis infrared sensing device corresponding to the vehicle to be identified is taken as the maximum vehicle width of the vehicle to be identified.

[0042] The vehicle chassis height of the vehicle to be identified can be determined by a chassis height prediction model based on the Y-axis obstacle distance collected by each target Y-axis infrared sensor at a target time point and the three-dimensional coordinate information of each target Y-axis infrared sensor, as well as the X-axis obstacle distance collected by each target X-axis infrared sensor at a target time point and the three-dimensional coordinate information of each target X-axis infrared sensor, wherein the chassis height prediction model can be a Convolutional Neural Networks (CNN) model.

[0043] Figure 2 is a flow chart of predicting the three-dimensional coordinates of the license plate of a vehicle to be identified shown in an embodiment of the present application, such as Figure 2 As shown, further, the parameter determination unit predicts the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle model parameters of the vehicle to be identified, including: Obtaining vehicle model parameters and relative license plate positions of multiple test vehicles, wherein the relative license plate position is used to characterize the relative position between the center of the license plate of the test vehicle and the center points of two front wheels of the test vehicle; Based on the vehicle model parameters of the multiple test vehicles, the multiple test vehicles are clustered to determine multiple vehicle clusters; Determine the three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor, and the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point and the three-dimensional coordinate information of each X-axis infrared sensor; Determine a similar vehicle cluster from multiple vehicle clusters based on the vehicle model parameters of the vehicle to be identified; Based on the model parameters of the vehicle to be identified, determine a similar test vehicle from a cluster of similar vehicles; Based on the relative position of the license plate of a similar test vehicle and the three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified, the three-dimensional coordinates of the license plate of the vehicle to be identified are predicted, wherein the three-dimensional coordinates of the license plate of the vehicle to be identified include the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified.

[0044] Specifically, for any two test vehicles, the vehicle model similarity of the two test vehicles can be calculated based on the vehicle model parameters of the two test vehicles, and a clustering algorithm (for example, a K-means clustering algorithm, a hierarchical clustering algorithm, etc.) can be used to cluster multiple test vehicles according to the vehicle model similarity of any two test vehicles to determine multiple vehicle clusters.

[0045] The model similarity of two test vehicles can be calculated based on the following formula:

[0046] in, is the similarity of the models of the two test vehicles, ,and is the weight, and greater than 0, , and is the preset parameter, and greater than 0, is the maximum width of the e-th test vehicle, is the maximum width of the f-th test vehicle, is the maximum length of the e-th test vehicle, is the maximum length of the f-th test vehicle, is the maximum height of the e-th test vehicle, is the maximum height of the f-th test vehicle, is the chassis height of the e-th test vehicle, is the chassis height of the f-th test vehicle.

[0047] The three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified can be determined by a first coordinate prediction model based on the Y-axis obstacle distance collected by each target Y-axis infrared sensor at a target time point and the three-dimensional coordinate information of each target Y-axis infrared sensor, as well as the X-axis obstacle distance collected by each target X-axis infrared sensor at a target time point and the three-dimensional coordinate information of each target X-axis infrared sensor, wherein the first coordinate prediction model can be a generative adversarial network (GAN) model.

[0048] The vehicle model similarity between the vehicle to be identified and the cluster center of the similar vehicle cluster can be calculated based on the vehicle model parameters of the vehicle to be identified and the vehicle model parameters corresponding to the cluster center of the similar vehicle cluster. The method of calculating the vehicle model similarity between the vehicle to be identified and the cluster center of the similar vehicle cluster is similar to the method of calculating the vehicle model similarity between two test vehicles, which will not be repeated here.

[0049] The vehicle clusters whose vehicle model similarity is greater than the first vehicle model similarity threshold are regarded as similar vehicle clusters. The vehicle model similarity between the vehicle to be identified and each test vehicle included in the similar vehicle clusters is calculated based on the vehicle model parameters of the vehicle to be identified and the vehicle model parameters of each test vehicle included in the similar vehicle clusters, and the test vehicles included in the similar vehicle clusters whose vehicle model similarity is greater than the second vehicle model similarity threshold are regarded as similar vehicles.

[0050] The three-dimensional coordinates of the license plate of the vehicle to be identified are predicted by a second coordinate model based on the relative positions of the license plates of similar test vehicles and the three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified, wherein the second coordinate model can be a generative adversarial network model.

[0051] The image acquisition module can be used to determine whether to adjust the image acquisition posture based on the predicted three-dimensional coordinates of the license plate of the vehicle to be identified. If it is determined that the image acquisition posture adjustment is required, a target image acquisition posture is generated based on the three-dimensional coordinates of the license plate of the vehicle to be identified, and the posture of the image acquisition device is adjusted based on the target image acquisition posture. After the posture is adjusted, the image of the vehicle to be identified is acquired. If it is determined that the image acquisition posture adjustment is not required, the image of the vehicle to be identified is acquired.

[0052] Furthermore, the image acquisition module determines whether to adjust the image acquisition posture based on the predicted license plate position of the vehicle to be identified, including: Acquire a plurality of first training samples, wherein the first training samples include a sample image acquisition posture, an image acquired by an image acquisition device in the sample image acquisition posture, and three-dimensional coordinates of a plurality of markers, and labels of the first training samples are pixel coordinates of the plurality of markers; Establishing a pixel coordinate prediction model, wherein the pixel coordinate prediction model may be a generative adversarial network model; Training a pixel coordinate prediction model using a plurality of first training samples; Using the trained pixel coordinate prediction model, based on the current image acquisition posture and the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified, predict the first pixel coordinates of the four corners of the license plate of the vehicle to be identified; Based on the first pixel coordinates of the four corners of the license plate of the vehicle to be identified, it is determined whether to adjust the image acquisition posture.

[0053] Specifically, when the first pixel coordinate of at least one corner exceeds the pixel coordinate range corresponding to the current image acquisition posture, it is determined that the image acquisition posture adjustment is performed. If the first pixel coordinates of the four corners of the license plate of the vehicle to be identified are all within the pixel coordinate range corresponding to the current image acquisition posture, it is determined that the image acquisition posture adjustment is not performed.

[0054] Furthermore, the image acquisition module generates a target image acquisition posture based on the license plate position of the vehicle to be identified, including: Acquire multiple second training samples, wherein the second training samples include the sample image acquisition posture before adjustment and the three-dimensional coordinates of multiple markers, and the labels of the second training samples are the sample image acquisition posture after adjustment; Establishing a posture adjustment model, wherein the posture adjustment model may be a generative adversarial network model; Training the pose adjustment model through a plurality of second training samples; Using the trained pose adjustment model, the target image acquisition pose is generated based on the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified.

[0055] The license plate recognition module can be used to recognize the license plate information of the vehicle to be recognized based on the image of the vehicle to be recognized.

[0056] Specifically include: Predicting the second pixel coordinates of the four corners of the license plate of the vehicle to be identified by using a pixel coordinate prediction model based on the target image acquisition posture and the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified; Extracting a target area image from the image of the vehicle to be identified based on the predicted second pixel coordinates of the four corners of the license plate of the vehicle to be identified; Based on the target area image, the license plate information of the vehicle to be identified is identified.

[0057] Specifically, the target area image may be extracted from the image of the vehicle to be identified by taking the second pixel coordinates of the four corners of the license plate of the vehicle to be identified as the pixel coordinates of the four corners of the target area image.

[0058] Furthermore, the license plate recognition module recognizes the license plate information of the vehicle to be recognized based on the target area image, including: Based on the target area image, extract the background area image and multiple license plate character images; Based on the background area image and the multiple license plate character images, determining whether the license plate of the vehicle to be identified is genuine; When it is determined that the license plate of the vehicle to be identified is genuine, optical character recognition is performed on the multiple license plate character images to determine the license plate number of the vehicle to be identified; When the license plate of the vehicle to be identified is determined to be fake, a warning message is generated.

[0059] Specifically, the fonts of real license plates have unique designs, good visual effects, and uniform font sizes and structures. Fake license plates may be too thin, too fat, or abnormal, or there may be abnormalities in the font corners. In addition, it is also possible to detect whether there are traces of alteration, covering, or pasting on the license plate, such as pasting white or black self-adhesive fonts on the license plate, or hollowing out or cutting out the fonts on the real license plate for patching.

[0060] Genuine license plates are made using one-shot molding technology, with uniform overall shape and neat corners, arcs and other details. Fake license plates may have problems such as different shapes and sizes. Genuine license plates have better paint quality, uniform coloring, and only coloring on the protruding parts. Fake license plates may have problems such as poor paint quality and uneven coloring.

[0061] Based on the above-mentioned distinction between genuine and fake license plates, a license plate authenticity recognition model can be used to determine whether the license plate of the vehicle to be identified is genuine based on the background area image and multiple license plate character images, wherein the license plate authenticity recognition model can be a convolutional neural network model.

[0062] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A vehicle barrier automatic license plate recognition control system, characterized in that: include: Vehicle sensing module, used to sense whether there is a vehicle in the lane; A vehicle identification module, comprising an infrared sensing unit and a parameter determination unit, wherein the infrared sensing unit comprises a plurality of X-axis infrared sensing devices arranged along the width direction of the barrier and a plurality of Y-axis infrared sensing devices arranged along the length direction of the barrier, the X-axis infrared sensing device comprises a plurality of X-axis infrared sensors arranged along the length direction of the barrier, and the Y-axis infrared sensing device comprises a plurality of Y-axis infrared sensors arranged along the vertical direction, the infrared sensing unit is used to collect infrared sensing information of the vehicle when the vehicle sensing module senses that there is a vehicle in the lane, and the parameter determination unit is used to predict the three-dimensional coordinates of the license plate of the vehicle to be identified based on the infrared sensing information of the vehicle; An image acquisition module is used to determine whether to adjust the image acquisition posture based on the predicted three-dimensional coordinates of the license plate of the vehicle to be identified. If it is determined that the image acquisition posture is to be adjusted, a target image acquisition posture is generated based on the three-dimensional coordinates of the license plate of the vehicle to be identified, and the posture of the image acquisition device is adjusted based on the target image acquisition posture. After the posture is adjusted, an image of the vehicle to be identified is acquired. If it is determined that the image acquisition posture is not to be adjusted, an image of the vehicle to be identified is acquired; The license plate recognition module is used to recognize the license plate information of the vehicle to be recognized based on the image of the vehicle to be recognized.

2. A vehicle barrier automatic license plate recognition control system according to claim 1, characterized in that: When the vehicle sensing module senses that there is a vehicle in the lane, the infrared sensing unit collects vehicle infrared sensing information, including: Start each Y-axis infrared sensor to collect the Y-axis obstacle distance; Each of the X-axis infrared sensors is started to collect the X-axis obstacle distance, wherein the vehicle infrared sensing information at least includes the Y-axis obstacle distance and the X-axis obstacle distance.

3. A vehicle barrier automatic license plate recognition control system according to claim 2, characterized in that: The parameter determination unit predicts the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle infrared sensing information, including: Determine vehicle posture change parameters of the vehicle to be identified at the multiple consecutive time points based on the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at multiple consecutive time points and the X-axis obstacle distances collected by each of the X-axis infrared sensors at multiple consecutive time points; Determining a target time point from the plurality of consecutive time points based on a vehicle posture change parameter of the vehicle to be identified at the plurality of consecutive time points; Based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the target time point and the X-axis obstacle distance collected by each of the X-axis infrared sensors at the target time point, the vehicle model parameters of the vehicle to be identified are determined, wherein the vehicle model parameters of the vehicle to be identified at least include the maximum width of the vehicle, the maximum length of the vehicle, the maximum height of the vehicle, and the chassis height of the vehicle; Based on the vehicle model parameters of the vehicle to be identified, the three-dimensional coordinates of the license plate of the vehicle to be identified are predicted.

4. A vehicle barrier automatic license plate recognition control system according to claim 3, characterized in that: The parameter determination unit determines the vehicle posture change parameters of the vehicle to be identified at the multiple continuous time points based on the Y-axis obstacle distances collected by each of the Y-axis infrared sensors at the multiple continuous time points and the X-axis obstacle distances collected by each of the X-axis infrared sensors at the multiple continuous time points, including: For each of the time points, based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the time point and the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the previous time point, calculate the Y-axis change parameter at the time point; For each of the time points, based on the X-axis obstacle distance collected by each of the X-axis infrared sensors at the time point and the X-axis obstacle distance collected by each of the X-axis infrared sensors at the previous time point, calculating the X-axis change parameter at the time point; For each of the time points, a weighted sum is taken of the Y-axis change parameter at the time point and the X-axis change parameter at the time point to obtain the vehicle posture change parameter of the vehicle to be identified at the time point.

5. The vehicle barrier automatic license plate recognition control system according to claim 3 is characterized in that: The parameter determination unit determines the vehicle model parameter of the to-be-identified vehicle based on the Y-axis obstacle distance collected by each of the Y-axis infrared sensors at the target time point and the X-axis obstacle distance collected by each of the X-axis infrared sensors at the target time point, including: Determine the maximum length and maximum height of the vehicle to be identified based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor; Determine the maximum width of the vehicle to be identified based on the X-axis obstacle distance collected by each of the X-axis infrared sensors at the target time point and the three-dimensional coordinate information of each of the X-axis infrared sensors; Based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor, and the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point and the three-dimensional coordinate information of each X-axis infrared sensor, the vehicle chassis height of the vehicle to be identified is determined.

6. The vehicle barrier automatic license plate recognition control system according to claim 3 is characterized in that: The parameter determination unit predicts the three-dimensional coordinates of the license plate of the vehicle to be identified based on the vehicle model parameters of the vehicle to be identified, including: Obtaining vehicle model parameters and relative license plate positions of multiple test vehicles, wherein the relative license plate position is used to characterize the relative position between the center of the license plate of the test vehicle and the center points of two front wheels of the test vehicle; Based on the vehicle model parameters of the multiple test vehicles, clustering the multiple test vehicles to determine multiple vehicle clusters; Determine the three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified based on the Y-axis obstacle distance collected by each Y-axis infrared sensor at the target time point and the three-dimensional coordinate information of each Y-axis infrared sensor, and the X-axis obstacle distance collected by each X-axis infrared sensor at the target time point and the three-dimensional coordinate information of each X-axis infrared sensor; Determining a similar vehicle cluster from the plurality of vehicle clusters based on the vehicle model parameters of the vehicle to be identified; Determining a similar test vehicle from the similar vehicle cluster based on the vehicle model parameters of the vehicle to be identified; Based on the relative position of the license plate of the similar test vehicle and the three-dimensional coordinates of the center points of the two front wheels of the vehicle to be identified, the three-dimensional coordinates of the license plate of the vehicle to be identified are predicted, wherein the three-dimensional coordinates of the license plate of the vehicle to be identified include the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified.

7. A vehicle barrier automatic license plate recognition control system according to claim 6, characterized in that: The image acquisition module determines whether to adjust the image acquisition posture based on the predicted license plate position of the vehicle to be identified, including: Acquire a plurality of first training samples, wherein the first training samples include a sample image acquisition posture, an image acquired by an image acquisition device in the sample image acquisition posture, and three-dimensional coordinates of a plurality of markers, and labels of the first training samples are pixel coordinates of the plurality of markers; Establish a pixel coordinate prediction model; Training the pixel coordinate prediction model using the multiple first training samples; Using the trained pixel coordinate prediction model, based on the current image acquisition posture and the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified, predict the first pixel coordinates of the four corners of the license plate of the vehicle to be identified; Based on the first pixel coordinates of the four corners of the license plate of the vehicle to be identified, it is determined whether to adjust the image acquisition posture.

8. The vehicle barrier automatic license plate recognition control system according to claim 7, characterized in that: The image acquisition module generates a target image acquisition posture based on the license plate position of the vehicle to be identified, including: Acquire a plurality of second training samples, wherein the second training samples include the sample image acquisition posture before adjustment and the three-dimensional coordinates of a plurality of markers, and the labels of the second training samples are the sample image acquisition posture after adjustment; Establish a posture adjustment model; Training the posture adjustment model using the plurality of second training samples; The trained posture adjustment model is used to generate the target image acquisition posture based on the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified.

9. A vehicle barrier automatic license plate recognition control system according to claim 7 or 8, characterized in that: The license plate recognition module recognizes the license plate information of the vehicle to be recognized based on the image of the vehicle to be recognized, including: Predicting second pixel coordinates of the four corners of the license plate of the vehicle to be identified by the pixel coordinate prediction model based on the target image acquisition posture and the three-dimensional coordinates of the four corners of the license plate of the vehicle to be identified; Extracting a target area image from the image of the vehicle to be identified based on the predicted second pixel coordinates of the four corners of the license plate of the vehicle to be identified; Based on the target area image, the license plate information of the vehicle to be identified is identified.

10. A vehicle barrier automatic license plate recognition control system according to claim 9, characterized in that: The license plate recognition module recognizes the license plate information of the vehicle to be recognized based on the target area image, including: Based on the target area image, extracting a background area image and a plurality of license plate character images; Based on the background area image and the plurality of license plate character images, determining whether the license plate of the vehicle to be identified is genuine; When it is determined that the license plate of the vehicle to be identified is authentic, optical character recognition is performed on the multiple license plate character images to determine the license plate number of the vehicle to be identified; When it is determined that the license plate of the vehicle to be identified is fake, a warning message is generated.

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