Vehicle detection method, device and equipment and readable storage medium

Abstract

The embodiment of the invention provides a vehicle detection method, device and equipment and a readable storage medium, and relates to the field of industrial automation detection, and the method comprises the steps: obtaining the vehicle type data of a vehicle; according to the vehicle type data, a first control instruction is sent to the camera shooting unit, and the first control instruction is used for adjusting the position of the camera shooting unit; a second control instruction is sent to the camera shooting unit, and the second control instruction is used for calling the camera shooting unit to shoot the to-be-detected area of the vehicle to obtain image data; and receiving the image data sent by the camera shooting unit, and performing part state identification on the to-be-detected area expressed by the image data to obtain a part state result and storing the part state result. And the management efficiency of part identification records is improved to a certain extent.

Description

Technical Field

[0001] The present application relates to the field of industrial automation detection, and in particular to a vehicle detection method, device, equipment and readable storage medium. Background Art

[0002] With the development of new energy vehicles and intelligent manufacturing, quality control requirements in the automobile manufacturing process are becoming increasingly stringent.

[0003] In the related art, manual inspection is used to check and repair parts in the vehicle.

[0004] However, traditional manual inspection is inefficient and error-prone, and cannot meet the high-precision vehicle production requirements. Summary of the Invention

[0005] The embodiments of the present application provide a vehicle detection method, apparatus, device, and readable storage medium. The technical solution is as follows:

[0006] In one aspect, a vehicle detection method is provided, the method comprising:

[0007] Acquiring vehicle model data of a vehicle to be inspected, wherein the vehicle model data is used to characterize a first position of the vehicle in a vehicle inspection production line;

[0008] Sending a first control instruction to a camera unit according to the vehicle model data, wherein the first control instruction is used to adjust a second position of the camera unit, the camera unit being a pre-configured unit for performing image acquisition on the vehicle on the vehicle inspection production line;

[0009] Sending a second control instruction to the camera unit, wherein the second control instruction is used to call the camera unit to shoot the area to be detected of the vehicle to obtain image data;

[0010] The image data sent by the camera unit is received, and part status recognition is performed on the area to be detected expressed by the image data to obtain and store a part status result, wherein the part status result is used to indicate the part status of the part in the area to be detected.

[0011] In another aspect, a vehicle detection device is provided, comprising:

[0012] An acquisition module, configured to acquire vehicle model data of a vehicle to be inspected, wherein the vehicle model data is used to characterize a first position of the vehicle in a vehicle inspection production line;

[0013] a sending module, configured to send a first control instruction to a camera unit based on the vehicle model data, wherein the first control instruction is used to adjust a second position of the camera unit, the camera unit being a pre-configured unit for performing image acquisition on the vehicle on the vehicle inspection production line;

[0014] The sending module is further configured to send a second control instruction to the camera unit, wherein the second control instruction is configured to call the camera unit to photograph the area to be detected of the vehicle to obtain image data;

[0015] An identification module is used to receive the image data sent by the camera unit, and to identify the part status of the area to be detected expressed by the image data, obtain and store the part status result, and the part status result is used to indicate the part status of the part in the area to be detected.

[0016] On the other hand, a computer-readable storage medium is provided, wherein at least one program is stored in the computer-readable storage medium, and the at least one program is loaded and executed by a processor to implement the vehicle detection method as described above.

[0017] On the other hand, a computer program product or computer program is provided, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes to implement the vehicle detection method as described above.

[0018] The beneficial effects of the technical solutions provided in the embodiments of the present application include at least:

[0019] According to the model data of the vehicle to be inspected, the position of the camera unit is adaptively adjusted so that the camera unit can capture image data containing more effective information (the effective information here refers to more parts, etc.) when shooting different vehicles, thereby improving the shooting efficiency and accuracy of the image data; in addition, after obtaining the image data, the part status corresponding to the parts in the image is automatically identified, solving the problem of low recognition efficiency caused by the use of manual recognition methods in related technologies, and providing support for subsequent storage or reading of part recognition records. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 is a schematic diagram of an execution architecture of a vehicle detection method according to an exemplary embodiment of the present application;

[0022] Figure 2 is a flow chart of a vehicle detection method provided by an exemplary embodiment of the present application;

[0023] Figure 3 is a system architecture diagram of a vehicle chassis inspection visual inspection system provided by an exemplary embodiment of the present application;

[0024] Figure 4 is a structural block diagram of a vehicle detection device provided by an exemplary embodiment of the present application;

[0025] Figure 5 is a structural block diagram of a vehicle detection device provided by another exemplary embodiment of the present application;

[0026] Figure 6 It is a structural block diagram of a computer device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of this application more clear, the following will further describe the embodiments of this application in detail with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0028] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first" and "second", nor is there any limitation on the quantity and execution order.

[0029] Please refer to Figure 1 , which shows a schematic diagram of an execution architecture of a vehicle detection method shown in an exemplary embodiment of the present application, wherein the execution architecture includes a vehicle 100 , a terminal 101 and a camera unit 102 .

[0030] Optionally, the vehicle 100 is a vehicle to be inspected, and during the inspection process, the vehicle 100 is placed on a conveyor belt on a vehicle inspection production line.

[0031] The vehicle inspection production line refers to a quality checkpoint in the vehicle assembly workshop. Before the vehicle 100 leaves the production line and is delivered to the user, or during the quality inspection process, the vehicle function and vehicle configuration need to be inspected.

[0032] The vehicle inspection production line is provided with a camera unit 102 , which is a pre-set unit for performing image acquisition on the vehicle 100 located on the vehicle inspection production line.

[0033] A communication connection is established between the camera unit 102 and the terminal 101 , and the communication connection is implemented as either a wireless communication connection or a wired communication connection.

[0034] In the embodiment of the present application, the terminal 101 obtains the vehicle model data of the vehicle 100 , and the vehicle model data is used to characterize and identify the vehicle 100 .

[0035] The terminal 101 sends a first control instruction for adjusting a first position of the camera unit 102 to the camera unit 102 according to the vehicle model data.

[0036] After the camera unit 102 completes the adjustment, the terminal 101 sends a second control instruction to the camera unit 102 to shoot the area to be detected of the vehicle.

[0037] The terminal 101 receives the image data sent by the camera unit 102, performs part status recognition on the area to be detected expressed by the image data, obtains and stores the part status result corresponding to the part.

[0038] Optionally, the vehicle 100 includes at least one of a fuel vehicle, an electric vehicle, a hybrid vehicle, a fuel cell vehicle, a solar vehicle, etc., wherein a hybrid vehicle refers to a combination of a fuel vehicle and an electric vehicle. This application does not limit the specific type of vehicle.

[0039] The above-mentioned terminal 101 is optional. The terminal can be a desktop computer, a laptop computer, a mobile phone, a tablet computer, a virtual reality (VR) device, an augmented reality (AR) device, a mixed reality (MR) device, an e-book reader, a Moving Picture Experts Group Audio Layer III I (MP3) player, a Moving Picture Experts Group Audio Layer IV (MP4) player, a smart TV, a smart car, and other terminal devices in various forms. The embodiments of the present application are not limited to this.

[0040] In another optional embodiment, the vehicle inspection method provided in the embodiments of the present application is implemented jointly by a server and terminal 101. Specifically, terminal 101 uploads image data captured by camera unit 102 to the server, which then performs part status recognition on the inspected area represented by the image data to obtain a part status result. The server then transmits the zero-year status result to terminal 101 for display and storage.

[0041] The terminal 101 and the server are connected via wireless communication or wired communication.

[0042] It is worth noting that the above-mentioned servers can be independent physical servers, or they can be server clusters or distributed systems composed of multiple physical servers. They can also be cloud servers that provide basic cloud computing services such as cloud services, cloud security, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), as well as big data and artificial intelligence platforms.

[0043] In some embodiments, the above-mentioned server can also be implemented as a node in a blockchain system.

[0044] It should be noted that the information (including but not limited to vehicle information, etc.), data (including but not limited to data used for analysis, storage, display, etc.), and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant region. For example, the construction material information involved in this application was obtained with full authorization.

[0045] To further explain, this application can display a prompt interface, pop-up window or output voice prompt information before collecting relevant user data (for example: the engineering material list corresponding to the vehicle involved in this application, etc.) and during the process of collecting relevant user data. The prompt interface, pop-up window or voice prompt information is used to remind the user that its relevant data is currently being collected, so that this application only starts to execute the relevant steps of obtaining user-related data after obtaining the user's confirmation operation on the prompt interface or pop-up window. Otherwise (that is, when the user's confirmation operation on the prompt interface or pop-up window is not obtained), the relevant steps of obtaining user-related data are terminated, that is, the user's relevant data is not obtained. In other words, all user data collected by this application are collected with the user's consent and authorization, and the collection, use and processing of relevant user data need to comply with the relevant laws, regulations and standards of the relevant region.

[0046] Combined with the above introduction, Figure 2This is a flow chart of a vehicle detection method provided in an embodiment of the present application, and this solution is applied to Figure 1 The terminal 101 shown is used as an example for explanation.

[0047] Step 200: Obtain vehicle model data.

[0048] The vehicle detection method provided in the embodiment of the present application is applied to a vehicle detection system, which includes a terminal, a vehicle, and a camera unit.

[0049] Optionally, the vehicle is a vehicle to be inspected. Schematically, the vehicle is a vehicle to be inspected after assembly, a vehicle to be inspected due to vehicle defects, or a vehicle to be inspected during normal quality inspection, etc. This application does not limit this.

[0050] Model data is used to identify the model of the vehicle.

[0051] The vehicle model data includes the vehicle identification number (VIN code).

[0052] The VIN code is a 17-digit alphanumeric code that uniquely identifies a vehicle. It includes, but is not limited to, information such as the manufacturer, vehicle features, year of production, location of production, and serial number.

[0053] In the embodiment of the present application, the first three digits of the VIN code are used to indicate the manufacturer code of the vehicle, the fourth to ninth digits of the VIN code are used to indicate the vehicle characteristics of the vehicle, including but not limited to the vehicle model, body size, engine model, etc., the tenth digit of the VIN code is used to indicate the production year of the vehicle, the eleventh digit of the VIN code is used to indicate the production place of the vehicle, and the twelfth to seventeenth digits of the VIN code are used to indicate the production serial number of the vehicle.

[0054] Schematically, the VIN code is implemented as abcAY60e8JN123456, where abc means the vehicle's manufacturer is a certain manufacturer, AY60e refers to the vehicle's vehicle characteristics, J means the vehicle's production year is 2018, N means the vehicle's production place is a certain factory, and 12345 refers to the vehicle's production serial number.

[0055] In another optional embodiment, the ninth digit of the VIN code is implemented as a check digit for verifying the authenticity of the VIN code. Schematically, AY60 refers to the vehicle feature of the vehicle, and e is the check digit corresponding to the vehicle.

[0056] Optionally, the VIN code is set at a preset location on the vehicle, including but not limited to the front windshield, door frame, engine compartment, etc. The VIN code can also be viewed through the driving license, insurance policy, etc. associated with the vehicle, which is not limited in this application.

[0057] In an embodiment of the present application, during the vehicle manufacturing, production, and maintenance processes, an Automatic Vehicle Identification (AVI) system is used to obtain vehicle model data for vehicle tracking and identification. The AVI system is integrated into a terminal and establishes a communication connection with a fixed label scanning unit and / or fixed barcode scanning unit installed in the vehicle inspection production line.

[0058] Optionally, each vehicle is assigned a radio frequency identification (RFID) tag, which contains the vehicle's model data. Once a vehicle enters the vehicle inspection line, the AVI system uses a fixed tag scanning unit to scan the RFID tag attached to the vehicle to obtain the vehicle's model data.

[0059] Optionally, the vehicle is assigned a corresponding barcode, in which the vehicle model data is written. After the vehicle enters the vehicle inspection production line, the AVI system obtains the vehicle data by scanning the barcode set on the vehicle body through a fixed barcode scanning unit.

[0060] It should be noted that the AVI system can also obtain vehicle model data through other methods, which is not limited in this application.

[0061] Step 210: Send a first control instruction to the camera unit according to the vehicle type data.

[0062] Optionally, the camera unit is a pre-set unit for performing image acquisition on vehicles on a vehicle inspection production line, and the terminal and the camera unit are connected via wireless communication or wired communication.

[0063] The first control instruction is used to adjust the position of the camera unit.

[0064] In some embodiments, the vehicle's dimensions are obtained from the vehicle model data, and a first control instruction is generated based on the dimensions. In an exemplary embodiment, the fourth through ninth digits of the VIN code are extracted, and the vehicle's dimensions are determined based on the fourth through ninth digits; and the first control instruction is generated based on the dimensions.

[0065] The vehicle body size includes the width and height information of the vehicle.

[0066] In the embodiment of the present application, the process of generating the first control instruction according to the vehicle body size includes any one or more of the following methods.

[0067] 1) In response to the width information of the vehicle being greater than a first width threshold, generating a first control instruction, the first control instruction being used to adjust the camera unit to be away from the vehicle in a horizontal direction.

[0068] 2) In response to the width information of the vehicle being less than a second width threshold, generating a first control instruction, the first control instruction being used to adjust the camera unit to approach the vehicle in a horizontal direction.

[0069] 3) In response to the width of the vehicle being greater than or equal to the second width threshold and less than or equal to the first width threshold, generating a first control instruction, the first control instruction being used to maintain the current position of the camera unit.

[0070] 4) In response to the vehicle height information being greater than a first height threshold, generating a first control instruction, the first control instruction being used to control the camera unit to rise in a vertical direction.

[0071] 5) In response to the vehicle height information being less than a second height threshold, generating a first control instruction, the first control instruction being used to control the camera unit to be lowered in a vertical direction.

[0072] 6) In response to the vehicle height information being greater than or equal to the second height threshold and less than or equal to the first height threshold, generating a first control instruction, the first control instruction being used to maintain the current position of the camera unit.

[0073] It should be noted that the above-mentioned processes of generating control instructions corresponding to the height information and width information can be applied in combination.

[0074] Step 220: Send a second control instruction to the camera unit.

[0075] Optionally, after the camera unit is completely adjusted according to the first control instruction, the camera unit generates an adjustment completion instruction.

[0076] An adjustment completion instruction is received, and a second control instruction is generated based on the adjustment completion instruction.

[0077] The second control instruction is used to call the camera unit to shoot the area to be detected of the vehicle.

[0078] In an embodiment of the present application, the process of generating the second control instruction includes any one of the following methods.

[0079] The first type: a preset shooting angle table is stored in the terminal, and the preset shooting angle table stores the corresponding relationship between vehicle model data and shooting angles.

[0080] A target shooting angle corresponding to the vehicle model data is obtained from a preset shooting angle table, and a first control instruction is generated based on the target shooting angle. That is, the first control instruction is used to control the camera unit to shoot the vehicle's inspection area according to the target shooting angle.

[0081] Optionally, the fourth to ninth digits of the VIN code are extracted, and the body size of the vehicle is determined based on the characters of the fourth to ninth digits; the target shooting angle is obtained from a preset shooting angle table based on the body size, and a first control instruction is generated based on the target shooting angle.

[0082] The second type: a preset shooting trajectory table is stored in the terminal, and the preset shooting trajectory table stores the corresponding relationship between vehicle model data and shooting trajectories.

[0083] A target shooting trajectory corresponding to the vehicle model data is obtained from a preset shooting trajectory table, and a first control instruction is generated based on the target shooting trajectory. That is, the first control instruction is used to control the camera unit to shoot the vehicle's to-be-detected area according to the target shooting trajectory.

[0084] Optionally, the fourth to ninth digits of the VIN code are extracted, and the body size of the vehicle is determined based on the characters in the fourth to ninth digits; the target shooting trajectory is obtained from a preset shooting trajectory table based on the body size, and a first control instruction is generated based on the target shooting trajectory.

[0085] The third method is to obtain the target shooting angle corresponding to the vehicle model data from the preset shooting angle table, and obtain the target shooting trajectory corresponding to the vehicle model data from the preset shooting trajectory table, and generate a first control instruction based on the target shooting trajectory and the target shooting trajectory.

[0086] That is, the first control instruction is used to control the camera unit to shoot the area to be detected of the vehicle according to the target shooting angle and the target shooting trajectory.

[0087] Optionally, the fourth to ninth digits of the VIN code are extracted, and the body size of the vehicle is determined based on the characters of the fourth to ninth digits; the target shooting trajectory is obtained from a preset shooting trajectory table based on the body size, and the target shooting angle is obtained from a preset shooting angle table, and a first control instruction is generated based on the target shooting trajectory and the target shooting angle.

[0088] The above method is only an illustrative example, and this application does not limit the method for generating the second control instruction.

[0089] Step 230 : receiving the image data sent by the camera unit, and performing part status recognition on the area to be inspected expressed by the image data, obtaining and storing the part status result.

[0090] Optionally, the camera unit sends image data of the area to be detected taken by itself to the terminal.

[0091] The terminal has a built-in parts recognition model, which is used to identify the parts status of vehicle parts in the image.

[0092] In some embodiments, the part recognition model is implemented as any one of artificial intelligence models. Schematically, the part recognition model is implemented as a real-time target detection model YOLOv5 (You Only Look Once version 5) model.

[0093] The image data is identified through the part recognition model to obtain the part status result and store the part status.

[0094] Optionally, a part recognition model is used to mark parts in the image data and determine a part position of the part, where the part position refers to an installation position of the part in the vehicle.

[0095] Through the part recognition model, the parts are identified and their corresponding category labels are obtained.

[0096] The category label corresponds to the part status, and the category label includes a qualified sub-label, a defective sub-label, and a part model sub-label.

[0097] The qualified sub-label is used to indicate whether the part is qualified. The qualified sub-label includes a qualified label and a failed label.

[0098] The defect sub-label is used to indicate the appearance of the part, and the defect sub-label includes at least one of a crack label, a chipped corner label, a scratch label, and a dirty label.

[0099] The part model subtag refers to the part's model number.

[0100] Determine the part status results based on the category label and part location.

[0101] In an optional embodiment, the part recognition model directly recognizes the part status result corresponding to the part in the image data. Schematically, the part recognition model determines the part status corresponding to the category label and generates the part status result.

[0102] In another optional embodiment, the part recognition model outputs the part position and category label corresponding to the image data, and the terminal determines the part status result based on the part position and category label.

[0103] In the embodiment of the present application, the part status result indicates the part status of the part in the area to be inspected.

[0104] Schematically, the part position identified by the part recognition model is the engine installed on the vehicle chassis, and the category labels are the qualified label, the dirty label and the part model sub-label "WPxxHxxx"; based on the qualified label, the dirty label and the part model sub-label "WPxxHxxx", the part status result "WPxxHxxx model engine is qualified, and the engine is dirty" is generated.

[0105] In another optional embodiment, after the part status result is obtained, the recognition time of the part status result is acquired; and the part status result is stored according to the recognition time.

[0106] Optionally, in response to the part status result indicating that the part is unqualified, the part status result is stored in the abnormal database; in response to the part status result indicating that the part is qualified, the part status result is stored in the normal database. During the storage process, each part status result is stored according to the recognition time.

[0107] Optionally, in response to the part status result indicating that the part is unqualified, an early warning message is played, where the early warning message is used to indicate that the part is unqualified.

[0108] Among them, the warning information can be a text warning, a sound warning, an alarm light warning, a video warning, or a combination of any two or more of the above four warning types.

[0109] In some embodiments, in response to the part status result indicating that the part is unqualified, a stop inspection instruction is sent to each inspection device provided on the vehicle inspection production line.

[0110] In an embodiment of the present application, the position of the camera unit is adaptively adjusted according to the vehicle model data of the vehicle to be detected, so that the camera unit can capture image data containing more effective information (the effective information here refers to more parts, etc.) when shooting different vehicles, thereby improving the shooting efficiency and accuracy of the image data; in addition, after obtaining the image data, the part status corresponding to the part in the image is automatically identified, solving the problem of low recognition efficiency caused by the use of manual recognition methods in related technologies, providing support for subsequent storage or reading of part identification records, and improving the management efficiency of part identification records.

[0111] In combination with the above content, the following embodiments are described by taking the vehicle detection method provided by this application as an example of applying it to a vehicle chassis detection visual inspection system. Figure 3 As shown, Figure 3The following diagram illustrates the system architecture of a vehicle chassis visual inspection system provided by an exemplary embodiment of the present application. The system architecture diagram includes a sensor module 300, a camera module 310, a visual software module 320, a programmable logic controller (PLC) module 330, and an Internet of Things (IoT) module 340. The following details the functions of each module mentioned in the system architecture diagram.

[0112] Optionally, the sensor module 300 and the camera module 310 are provided on the vehicle inspection production line, and the visual software module 320, the PLC module 330 and the IOT module 340 are provided on the terminal. The terminal is connected to the sensor module 300 and the camera module 310 via a wired communication mode or a wireless communication mode.

[0113] The sensor module 300 is set in the vehicle detection production line and includes a photoelectric sensor. The photoelectric sensor detects whether the vehicle is located in the vehicle detection production line. When the photoelectric sensor detects that the vehicle is located in the vehicle detection production line, the encoder locates the vehicle's position information.

[0114] In some embodiments, the sensor module 300 further includes a mechanical sensor to detect whether the vehicle has collided.

[0115] Camera module 310 is a module installed in the collaborative robot in the vehicle inspection production line. Camera module 310 is used to capture the vehicle chassis and obtain image data. Illustratively, the collaborative robot is equipped with a preset camera unit, which is the camera module 310.

[0116] In some embodiments, the camera module 310 configures the exposure time according to the area to be detected of the vehicle chassis, and executes the shooting position corresponding to the target shooting trajectory and the target shooting angle according to the vehicle model data.

[0117] Optionally, the camera module 310 adjusts its initial shooting position based on the target shooting trajectory and the target shooting angle according to the vehicle model data, the target shooting trajectory and the target shooting angle.

[0118] The camera module 310 shoots the vehicle chassis at an initial shooting position according to a target shooting trajectory and a target shooting angle.

[0119] The visual software module 320 includes a vehicle model recognition unit, a PLC communication unit, a visual algorithm unit, a data management unit, and a visualization unit. The vehicle model recognition unit receives vehicle model data from the IoT module 340; the PLC communication unit communicates with the PLC module 330 via a pre-set protocol; the visual algorithm unit uses a part recognition model to identify part status; the data management unit stores part status results and vehicle model data; and the visualization unit displays the inspection image and part status results in real time.

[0120] The programmable logic controller (PLC) module 330 is implemented as a communication hub for synchronizing control instructions to the camera module 300. In some embodiments, the PLC module is used to write a code corresponding to the vehicle model data into the PLC register to trigger the collaborative robot program, and to execute the stop detection logic based on the part status results.

[0121] The IOT module 340 is used to upload the part status results to the cloud server, which stores the identification records to support remote query and analysis.

[0122] In some embodiments, the IOT module is set in the collaborative robot to enable the collaborative robot to store vehicle model data and parts status results.

[0123] In an embodiment of the present application, before executing the parts detection process, hardware deployment and parameter configuration are performed on the above-mentioned vehicle chassis detection system.

[0124] Hardware deployment includes the following:

[0125] The above-mentioned terminal is implemented as an industrial computer, which is used to run the visual algorithm. The PLC is implemented as Siemens S7-1200, and the collaborative robot is implemented as AUBO-i10R, which is connected to the PLC through an automation communication standard, where the automation communication standard is implemented as a Process Field Network.

[0126] The collaborative robot is installed in the middle of the vehicle inspection production line, covering the inspection area where the vehicle chassis is located.

[0127] Parameter configuration includes the following:

[0128] 1. PLC communication configuration.

[0129] Optionally, configure the IP address, rack number, and slot number, define the mapping between the vehicle model name and the PLC register value, and write the IP address.

[0130] Schematically, the PLC communication configuration is implemented through the following code.

[0131] "plc":{

[0132] "ip":"10.188.166.240",

[0133] "rack":0,

[0134] "slot":1,

[0135] "vehicle_type_map":{

[0136] "EH3":1,

[0137] "EHY":2,

[0138] "EH7":3},

[0139] "vehicle_type_address":0}";

[0140] The above code means that the PLC communication module is connected to the industrial computer through the IP address 10.188.166.240, and the rack number and slot number are configured as 0 and 1 respectively. The mapping relationship between the vehicle model name and the PLC register value is defined through vehicle_type_map. For example, the EH3 model corresponds to register value 1. The register address to which the vehicle model code is written is specified as 0 through vehicle_type_address.

[0141] 2. Collaborative robot detection area configuration.

[0142] Optionally, define the corresponding relationship between the detection area number and name, and configure the exposure time of the camera unit in each area. Schematically, the following code is used to implement the collaborative robot detection area configuration.

[0143] "robot_server":{

[0144] "pos_region_map":{

[0145] "1":"Front chassis guard - front left",

[0146] "2":"Front chassis guard - front right",

[0147] "3": "Front chassis guard - middle"},

[0148] "pos_exposure_map":{

[0149] "1":10000,

[0150] "2":10000,

[0151] "3":8000}}";

[0152] The above code means that the correspondence between the number and name of the detection area is defined through pos_region_map; and the exposure time of an independent camera unit is configured for each detection area through pos_exposure_map, in microseconds (μs).

[0153] 3. Part recognition model parameter configuration.

[0154] Optionally, specify the storage path, computing device, confidence threshold, and input image size of the part recognition model. Schematically, the following code implements the part recognition model parameter configuration.

[0155] ""model_config":{

[0156] "model_path":"resource / robot / z199.pt",

[0157] "device":"cpu",

[0158] "threshold":0.6,

[0159] "img_size":[4032, 3024]}";

[0160] The above code specifies the storage path of the part recognition model through model_path; device is set to "cpu" to indicate that the CPU is used for calculation; threshold is set to 0.6 as the confidence threshold for the detection result; and img_size is fixed to [4032, 3024] to ensure that the input image size is uniform.

[0161] 4. Exception handling.

[0162] Optionally, exception handling specifies the stop detection signal and alarm light signal register addresses and sets a global timeout threshold. Schematically, exception handling is implemented using the following code.

[0163] "plc":{

[0164] "stop_address":6,

[0165] "warning_light_address":8

[0166] "sys_address":22,

[0167] "sys_len":1}

[0168] "factory_config":{

[0169] "factory_timeout_t":5}";

[0170] The above code means that the register address of the emergency stop signal is specified as 6 through stop_address; the register address of the warning light signal is specified as 8 through warning_light_address; when communication is interrupted, the communication status is monitored through the system status register (such as address 22), and the system automatically retries three times before triggering the emergency stop signal and prompting through the warning light; the global timeout threshold is set to 5 seconds through factory_timeout_t; if any detection step is not completed within the time limit, the system automatically terminates the current process and records the error log.

[0171] 5. IOT server configuration.

[0172] Optionally, specify the service port, vehicle data file path for each vehicle model, and data upload interface.

[0173] Schematically, the IOT server configuration is implemented through the following code.

[0174] "iot_server":{

[0175] "port":10089,

[0176] "eh3_bom_path":"resource / robot / eh3_bom.xlsx",

[0177] "ehy_bom_path":"resource / robot / ehy_bom.xlsx",

[0178] "eh7_bom_path":"resource / robot / eh7_bom.xlsx"}

[0179] "factory_config":{

[0180] "iot_data_url":"http: / / 10.188.2.52:9501 / web / httpServlet / api / batteryPencilvisua lSendExecuteResult",}";

[0181] The above code means that the IOT service is running on port 10089. The model data file path of each model is configured independently to ensure accurate correspondence of model data. After the detection is completed, the system automatically packages the data and sends it to the iot_data_url interface via HTTP POST request, schematically:

[0182] "x_station_config":{

[0183] "obs_bucket_key":"wire_ch_postback",

[0184] "zip_map":{

[0185] "EH3":"004",

[0186] "EHY":"009"}};

[0187] The above code means that the system automatically associates the test results with production data: compresses the test data according to the vehicle model code (such as EH3→004); uploads it to an object (such as an industrial computer, cloud storage, etc.) for long-term storage, and returns a status code 200 on successful receipt.

[0188] 6. Cloud data configuration.

[0189] Optionally, set the Redis port and data channel. Use the following code to implement cloud data configuration.

[0190] "redis_config":{

[0191] "port":6379,

[0192] "data_channel":"ch_robot_data",}";

[0193] The above code means that the cloud realizes real-time communication through the publish-subscribe mode of the remote dictionary server, and the detection data is pushed through the ch_robot_data channel. In the embodiment of the present application, after the above hardware deployment and parameter configuration are completed, the vehicle chassis detection process is performed according to the following steps.

[0194] S1. The vehicle arrives and triggers the detection process.

[0195] Optionally, a photoelectric sensor detects the presence of a vehicle, sends a signal to an industrial computer, triggers a vehicle model query, and simultaneously captures the vehicle position information through an encoder.

[0196] S2, vehicle model matching and collaborative robots are executed according to the target shooting angle and target shooting trajectory.

[0197] Optionally, the vehicle chassis detection system reads the vehicle model information and obtains the corresponding PLC register value by querying vehicle_type_map; after receiving the vehicle model code, the PLC controls the robot to move to a preset position through the Profinet protocol.

[0198] S3. Image acquisition and detection.

[0199] Optionally, after the collaborative robot is in place, the image shooting process is executed. The camera unit shoots images according to the exposure parameters configured in pos_exposur e_map, loads the part recognition model (such as the YOLOv5 model) according to model_config, identifies the part status, and obtains the part status result.

[0200] The part status results are bound to the VIN code and timestamp data and stored in the database.

[0201] S4. Result feedback and alarm.

[0202] Optionally, in response to the part status result indicating that the part is unqualified, an audible and visual alarm is triggered, and the part status result is uploaded to the IOT system in real time to form a closed-loop management.

[0203] In an embodiment of the present application, the position of the camera unit is adaptively adjusted according to the vehicle model data of the vehicle to be detected, so that the camera unit can capture image data containing more effective information (the effective information here refers to more parts, etc.) when shooting different vehicles, thereby improving the shooting efficiency and accuracy of the image data; in addition, after obtaining the image data, the part status corresponding to the parts in the image is automatically identified, solving the problem of low recognition efficiency caused by the use of manual recognition methods in related technologies, and providing support for subsequent storage or reading of part recognition records.

[0204] See Figure 4 , which shows a structural block diagram of a vehicle detection device provided by an exemplary embodiment of the present application. The device includes the following contents.

[0205] An acquisition module 400 is configured to acquire vehicle model data of a vehicle to be detected, wherein the vehicle model data is used to identify the vehicle;

[0206] a sending module 410 configured to send a first control instruction to a camera unit based on the vehicle model data, wherein the first control instruction is configured to adjust a position of the camera unit, the camera unit being a pre-configured unit configured to capture images of the vehicles on the vehicle inspection production line;

[0207] The sending module 410 is further configured to send a second control instruction to the camera unit, wherein the second control instruction is configured to call the camera unit to capture the area to be detected of the vehicle to obtain image data;

[0208] The identification module 420 is used to receive the image data sent by the camera unit, and to identify the part status of the area to be detected expressed by the image data, obtain and store the part status result, and the part status result is used to indicate the part status of the part in the area to be detected.

[0209] In an optional embodiment, if Figure 5 As shown, the recognition module 420 is used to recognize the image data through a part recognition model to obtain the part status result;

[0210] The storage module 430 is configured to store the part status result.

[0211] In an optional embodiment, the recognition module 420 is configured to mark the part in the image data and determine the part position of the part using the part recognition model;

[0212] The identification module 420 is configured to identify the part by its category using the part identification model to obtain a category label corresponding to the part. The category label corresponds to the part status and includes a qualified sub-label, a defective sub-label, and a part model sub-label. The qualified sub-label indicates whether the part is qualified, the defective sub-label indicates the appearance status of the part, and the part model sub-label indicates the model of the part.

[0213] The identification module 420 is configured to determine the part status result according to the category label and the part position.

[0214] In an optional embodiment, the acquisition module 400 is configured to acquire a target shooting angle corresponding to the vehicle model data from a preset shooting angle table, wherein the preset shooting angle table stores a correspondence between the vehicle model data and the shooting angle;

[0215] The acquisition module 400 is configured to generate the second control instruction based on the target shooting angle;

[0216] The sending module 410 is further configured to send the second control instruction to the camera unit.

[0217] In an optional embodiment, the acquisition module 400 is configured to acquire a target shooting trajectory corresponding to the vehicle model data from a preset shooting trajectory table, wherein the preset shooting trajectory table stores a correspondence between the vehicle model data and the shooting trajectory;

[0218] The acquisition module 400 is configured to generate the second control instruction based on the target shooting trajectory;

[0219] The sending module 410 is further configured to send the second control instruction to the camera unit.

[0220] In an optional embodiment, the playing module 440 is configured to play a warning message in response to the part status result indicating that the part is unqualified, wherein the warning message is used to indicate that the part is unqualified;

[0221] The sending module 410 is further configured to send a stop detection instruction to the detection equipment on the vehicle detection production line.

[0222] In an embodiment of the present application, the position of the camera unit is adaptively adjusted according to the vehicle model data of the vehicle to be detected, so that the camera unit can capture image data containing more effective information (the effective information here refers to more parts, etc.) when shooting different vehicles, thereby improving the shooting efficiency and accuracy of the image data; in addition, after obtaining the image data, the part status corresponding to the parts in the image is automatically identified, solving the problem of low recognition efficiency caused by the use of manual recognition methods in related technologies, and providing support for subsequent storage or reading of part recognition records.

[0223] It should be noted that the vehicle detection device provided in the above embodiment is merely an example of the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle detection device provided in the above embodiment and the vehicle detection method embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0224] Figure 6The following is a block diagram of a computer device 600 provided in accordance with an exemplary embodiment of the present application. The computer device 600 may be a portable mobile terminal, such as a smartphone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, or a desktop computer. The computer device 600 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal, or other similar names. Optionally, the computer device 600 may also be implemented as a mobile device, such as a mobile smart terminal such as an in-vehicle terminal.

[0225] Typically, the computer device 600 includes a processor 601 and a memory 602 .

[0226] The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 601 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 601 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 601 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0227] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 602 is used to store at least one instruction, which is used to be executed by the processor 601 to implement the model training method or behavior coding method provided in the method embodiment of the present application.

[0228] In some embodiments, computer device 600 may optionally include a peripheral device interface 603 and at least one peripheral device. Processor 601, memory 602, and peripheral device interface 603 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 603 via a bus, signal lines, or circuit boards. For example, the peripheral device may include at least one of a radio frequency circuit 604, a display screen 605, a camera assembly 606, an audio circuit 607, a positioning assembly 615, and a power supply 608.

[0229] The peripheral device interface 603 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 601 and the memory 602. In some embodiments, the processor 601, the memory 602, and the peripheral device interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 601, the memory 602, and the peripheral device interface 603 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0230] The radio frequency circuit 604 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 604 communicates with communication networks and other communication devices via electromagnetic signals. The radio frequency circuit 604 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 604 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 604 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 604 may also include circuits related to Near Field Communication (NFC), which is not limited in this application.

[0231] Display screen 605 is used to display a user interface (UI). This UI can include graphics, text, icons, videos, or any combination thereof. If display screen 605 is a touchscreen display, it can also capture touch signals on or above the surface of display screen 605. These touch signals can be input as control signals to processor 601 for processing. Display screen 605 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 605, located on the front panel of computer device 600. In other embodiments, there can be at least two display screens 605, located on different surfaces of computer device 600 or in a foldable design. In still other embodiments, display screen 605 can be a flexible display screen, located on a curved or foldable surface of computer device 600. Display screen 605 can also be configured as a non-rectangular, irregular shape, also known as a special-shaped screen. Display screen 605 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0232] The camera assembly 606 is used to capture images or videos. Optionally, the camera assembly 606 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 606 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0233] The audio circuit 607 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals that are input into the processor 601 for processing, or input into the radio frequency circuit 604 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there can be multiple microphones, each located in different parts of the computer device 600. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert electrical signals from the processor 601 or the radio frequency circuit 604 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into sound waves audible to humans, but also convert electrical signals into sound waves inaudible to humans for purposes such as distance measurement. In some embodiments, the audio circuit 607 may also include a headphone jack.

[0234] The positioning component 615 is used to locate the current geographic location of the computing device 600 to implement navigation or LBS (Location Based Service). The positioning component 615 can be a positioning component based on the US GPS (Global Positioning System) or China's Beidou system.

[0235] Power supply 608 is used to power various components in computer device 600. Power supply 608 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 608 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0236] In some embodiments, the computer device 600 further includes one or more sensors 609 , including but not limited to: an acceleration sensor 610 , a gyroscope sensor 611 , a pressure sensor 612 , an optical sensor 613 , and a proximity sensor 614 .

[0237] The accelerometer 610 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the computer device 600. For example, the accelerometer 610 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 601 can control the display screen 605 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 610. The accelerometer 610 can also be used to collect game or user motion data.

[0238] The gyroscope sensor 611 can detect the orientation and rotation angle of the computer device 600. It can also work with the accelerometer 610 to collect 3D motions of the user on the computer device 600. Based on the data collected by the gyroscope sensor 611, the processor 601 can implement the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0239] The pressure sensor 612 can be installed on the side frame of the computer device 600 and / or below the display screen 605. When the pressure sensor 612 is installed on the side frame of the computer device 600, it can detect the user's grip signal of the computer device 600. The processor 601 can perform left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 612. When the pressure sensor 612 is installed below the display screen 605, the processor 601 controls the operational controls on the UI interface based on the user's pressure operation on the display screen 605. The operational controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0240] The optical sensor 613 is used to detect ambient light intensity. In one embodiment, the processor 601 can control the display brightness of the display screen 605 based on the ambient light intensity detected by the optical sensor 613. For example, when the ambient light intensity is high, the display brightness of the display screen 605 is increased; when the ambient light intensity is low, the display brightness of the display screen 605 is decreased. In another embodiment, the processor 601 can also dynamically adjust the shooting parameters of the camera assembly 606 based on the ambient light intensity detected by the optical sensor 613.

[0241] Proximity sensor 614, also known as a distance sensor, is typically located on the front panel of computer device 600. Proximity sensor 614 is used to detect the distance between the user and the front of computer device 600. In one embodiment, when proximity sensor 614 detects that the distance between the user and the front of computer device 600 is gradually decreasing, processor 601 controls display screen 605 to switch from the screen-on state to the screen-off state. When proximity sensor 614 detects that the distance between the user and the front of computer device 600 is gradually increasing, processor 601 controls display screen 605 to switch from the screen-off state to the screen-on state.

[0242] Those skilled in the art will understand that Figure 6 The structure shown in the figure does not constitute a limitation on the computer device 600, and the computer device 600 may include more or fewer components than shown in the figure, or combine some components, or adopt a different arrangement of components.

[0243] The present application also provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the vehicle detection method provided by the above method embodiment.

[0244] The present application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle detection method provided in the above method embodiment.

[0245] Those skilled in the art will appreciate that all or part of the steps in the above embodiments may be implemented by hardware or by programs instructing the relevant hardware to perform the steps. The programs may be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. The above are merely optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A vehicle detection method, characterized in that: The method comprises: Acquire vehicle model data of a vehicle to be detected, wherein the vehicle model data is used to identify the vehicle model; Sending a first control instruction to a camera unit according to the vehicle model data, wherein the first control instruction is used to adjust a position of the camera unit, the camera unit being a pre-set unit for performing image acquisition on the vehicle on the vehicle inspection production line; Sending a second control instruction to the camera unit, wherein the second control instruction is used to call the camera unit to shoot the area to be detected of the vehicle to obtain image data; The image data sent by the camera unit is received, and part status recognition is performed on the area to be detected expressed by the image data to obtain and store a part status result, wherein the part status result is used to indicate the part status of the part in the area to be detected.

2. The method according to claim 1, characterized in that The performing part status recognition on the area to be detected expressed by the image data, obtaining and storing a part status result, includes: Identify the image data using a parts recognition model to obtain the parts status result; The part status result is stored.

3. The method according to claim 2, characterized in that The identifying the image data by using a part identification model to obtain the part status result includes: Marking the part in the image data and determining the part position of the part by using the part recognition model; Using the part recognition model, the part is classified and a category label corresponding to the part is obtained. The category label corresponds to the part status and includes a qualified sub-label, a defective sub-label, and a part model sub-label. The qualified sub-label is used to indicate whether the part is qualified, the defective sub-label is used to indicate the appearance status of the part, and the part model sub-label is the model of the part. The part status result is determined according to the category label and the part position.

4. The method according to any one of claims 1 to 3, characterized in that: The sending a second control instruction to the camera unit includes: obtaining a target shooting angle corresponding to the vehicle model data from a preset shooting angle table, wherein the preset shooting angle table stores a correspondence between the vehicle model data and the shooting angle; generating a second control instruction based on the target shooting angle; The second control instruction is sent to the camera unit.

5. The method according to any one of claims 1 to 3, characterized in that: The sending a second control instruction to the camera unit includes: Acquire a target shooting trajectory corresponding to the vehicle model data from a preset shooting trajectory table, wherein the preset shooting trajectory table stores a correspondence between the vehicle model data and the shooting trajectory; generating a second control instruction based on the target shooting trajectory; The second control instruction is sent to the camera unit.

6. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: In response to the part status result indicating that the part is unqualified, playing a warning message, wherein the warning message is used to indicate that the part is unqualified; Send a stop detection instruction to the detection equipment on the vehicle detection production line.

7. A vehicle detection device, characterized in that: The device further comprises: An acquisition module, configured to acquire vehicle model data of a vehicle to be detected, wherein the vehicle model data is used to identify the vehicle; a sending module, configured to send a first control instruction to a camera unit based on the vehicle model data, wherein the first control instruction is used to adjust a position of the camera unit, the camera unit being a pre-set unit for performing image acquisition on the vehicles on the vehicle inspection production line; The sending module is further configured to send a second control instruction to the camera unit, wherein the second control instruction is configured to call the camera unit to photograph the area to be detected of the vehicle to obtain image data; An identification module is used to receive the image data sent by the camera unit, and to identify the part status of the area to be detected expressed by the image data, obtain and store the part status result, and the part status result is used to indicate the part status of the part in the area to be detected.

8. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one program, and the at least one program is loaded and executed by the processor to implement the vehicle detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The storage medium stores at least one program, and the at least one program is loaded and executed by the processor to implement the vehicle detection method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the vehicle detection method according to any one of claims 1 to 6.

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