Automobile station access monitoring method and system and readable storage medium

Abstract

The invention provides an automobile station entry and exit monitoring method and system and a readable storage medium, and the method comprises the steps: capturing a vehicle image in a preset region in real time through a deep learning algorithm, and detecting whether a vehicle enters and exits an automobile station; when a vehicle enters and exits from the vehicle station, a multi-target tracking algorithm based on a neural network tracks the moving path of the vehicle in the workshop to obtain tracking data, and the parking time of the vehicle at each vehicle station is recorded; identifying a license plate in the vehicle image through an OCR technology to obtain license plate information; transmitting the license plate information and the tracking data to a background system; and recording the entering time and the leaving time of the vehicle at the vehicle station, and storing the entering time and the leaving time in a database. Through the technical scheme of the invention, the vehicle management efficiency can be improved, the error rate of manual operation is reduced, and the method is suitable for high-precision monitoring and management requirements in a multi-vehicle scene.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle identification management, and in particular to a method and system for monitoring entry and exit of an automobile workstation, and a readable storage medium. Background Art

[0002] In car repair workshops, the detection of vehicles entering and exiting workstations is a key link. At present, vehicle management in scenes such as workshops or parking lots usually relies on manual recording or simple video monitoring. This method is not only inefficient, but also prone to omissions. Traditional infrared technology can be used for such tasks, but infrared sensors are mainly used to detect the presence and distance of objects, and it is difficult to distinguish the specific type or shape of objects. For complex scenes, such as when multiple vehicles enter and exit at the same time, accurate identification may not be possible, and infrared light is easily interfered by infrared light sources in the external environment (such as sunlight or other heat sources), thus affecting the accuracy of detection. Summary of the invention

[0003] This application aims to solve or improve the above technical problems.

[0004] To this end, the first purpose of the present application is to provide a method for monitoring entry and exit of an automobile workstation.

[0005] The second objective of the present application is to provide an automobile workstation entry and exit monitoring system.

[0006] The third objective of the present application is to provide an automobile workstation entry and exit monitoring system.

[0007] The fourth object of the present application is to provide a readable storage medium.

[0008] To achieve the first purpose of the present application, the technical solution of the first aspect of the present application provides a method for monitoring entry and exit of an automobile workstation, including: using a deep learning algorithm to capture vehicle images in a preset area in real time to detect whether there is a vehicle entering or exiting the automobile workstation; when a vehicle enters or exits the automobile workstation, a multi-target tracking algorithm based on a neural network tracks the movement path of the vehicle in the workshop to obtain tracking data, and records the parking time of the vehicle at each automobile workstation; using OCR technology to recognize the license plate in the vehicle image to obtain license plate information; transmitting the license plate information and tracking data to a background system; recording the entry and exit time of the vehicle at the automobile workstation, and storing the entry and exit time in a database.

[0009] According to the method for monitoring the entry and exit of a car workstation provided by the present application, firstly, a deep learning algorithm is used to capture the vehicle image in a preset area in real time to detect whether there is a vehicle entering or exiting the car workstation. The deep learning model is used to analyze the vehicle image in real time, which can filter background interference and ensure the accuracy of detection and tracking. When a vehicle enters or exits the car workstation, a multi-target tracking algorithm based on a neural network is used to track the movement path of the vehicle in the workshop, obtain tracking data, and record the parking time of the vehicle at each car workstation. The multi-target tracking algorithm based on a neural network can monitor each vehicle entering the monitoring area in real time and continuously record its position and movement path. Then, the license plate in the vehicle image is recognized by OCR (Optical Character Recognition) technology to obtain the license plate information. OCR technology can process license plate images under different angles and lighting conditions, and support license plate recognition in multiple languages ​​and character formats, ensuring that the license plate information can still be efficiently recognized in complex environments. Then the license plate information and tracking data are transmitted to the background system to ensure the real-time and accuracy of the data. Finally, the entry and exit time of the vehicle at the car workstation is recorded, and the entry and exit time are stored in the database for later query and management. The automobile workstation entry and exit monitoring method provided in this application can not only automatically identify vehicles, but also track vehicles in real time and extract license plate information when vehicles enter and exit, thereby realizing intelligent and automated vehicle management, improving the efficiency of vehicle management, and reducing the error rate of manual operations. It is suitable for high-precision monitoring and management needs in multi-vehicle scenarios.

[0010] In addition, the technical solution provided by this application may also have the following additional technical features: In some technical solutions, optionally, the method for monitoring the entry and exit of a vehicle workstation further includes: sending a warning notification to an administrator when license plate recognition fails or data transmission is abnormal.

[0011] In this technical solution, the method for monitoring the entry and exit of automobile workstations also includes sending a warning notification to an administrator when license plate recognition fails or data transmission is abnormal, thereby ensuring that the administrator can handle the abnormal situation in a timely manner and improving the reliability of the system.

[0012] In some technical solutions, optionally, a deep learning algorithm is used to capture vehicle images in a preset area in real time to detect whether there are vehicles entering or exiting the car workstation, including: acquiring vehicle images in a preset area through an image sensor; detecting vehicles in the vehicle images through a pre-trained neural network model to determine whether there are vehicles entering or exiting the car workstation.

[0013] In this technical solution, the deep learning algorithm is used to capture the vehicle images in the preset area in real time to detect whether there are vehicles entering or leaving the car station. Specifically, the vehicle images in the monitoring area are obtained through cameras or other image sensors, and the pre-trained neural network model is used to detect the vehicles in the vehicle images to determine whether there are vehicles entering or leaving the car station. Using the neural network-based object detection algorithm, after a large number of image training, various vehicles can be accurately detected, so that license plates can be efficiently recognized in complex environments, improving recognition accuracy and robustness.

[0014] In some technical solutions, optionally, the license plate information and tracking data are transmitted to the background system, including: transmitting the license plate information and tracking data to a remote server via a network, and the transmission method includes asynchronous data transmission and multi-threaded processing.

[0015] In this technical solution, the license plate information and tracking data are transmitted to the background system, specifically, the license plate information and tracking data are transmitted to the remote server through the network. The transmission method includes asynchronous data transmission and multi-threaded processing, which can ensure the real-time and integrity of the data in high-concurrency scenarios.

[0016] In some technical solutions, optionally, the license plate in the vehicle image is recognized by OCR technology to obtain the license plate information, including: preprocessing the license plate images at different angles and under different lighting conditions; recognizing the license plate in the preprocessed vehicle image by an OCR model to obtain the license plate information; and associating the license plate information with the tracking data.

[0017] In this technical solution, the license plate in the vehicle image is recognized by OCR technology to obtain the license plate information. Specifically, the license plate images under different angles and different lighting conditions are first preprocessed to improve the success rate of OCR recognition. Then, the license plate in the preprocessed vehicle image is recognized by the OCR model to obtain the license plate information. Finally, the license plate information is associated with the tracking data. By recognizing the license plate in the vehicle image by OCR technology, the license plate information can still be efficiently recognized in complex environments.

[0018] In some technical solutions, optionally, the method for monitoring the entry and exit of a vehicle workstation further includes: querying and exporting the license plate information and tracking data through a query interface.

[0019] In this technical solution, the vehicle station entry and exit monitoring method also includes querying and exporting license plate information and tracking data through a query interface. Through the query interface, managers can view vehicle entry and exit records at any time, and the system supports data export function to facilitate subsequent statistics and analysis.

[0020] In some technical solutions, optionally, the notification method of the warning notification includes email, text message or instant messaging tool.

[0021] In this technical solution, the warning notification method includes email, text message or instant messaging tool to remind the administrator in time.

[0022] To achieve the second purpose of the present application, the technical solution of the second aspect of the present application provides a system for monitoring entry and exit of an automobile workstation, including: a vehicle monitoring module, which is used to capture vehicle images in a preset area in real time through a deep learning algorithm to detect whether there is a vehicle entering or exiting the automobile workstation; a vehicle tracking module, which is used to track the movement path of the vehicle in the workshop based on a multi-target tracking algorithm of a neural network when a vehicle enters or exits the automobile workstation, obtain tracking data, and record the parking time of the vehicle at each automobile workstation; a license plate recognition module, which is used to recognize the license plate in the vehicle image through OCR technology to obtain license plate information; a data transmission module, which is used to transmit the license plate information and tracking data to a background system; a vehicle status recording module, which is used to record the entry time and exit time of the vehicle at the automobile workstation, and store the entry time and exit time in a database.

[0023] According to the automobile station entry and exit monitoring system provided by the present application, it includes a vehicle monitoring module, a vehicle tracking module, a license plate recognition module, a data transmission module and a vehicle status recording module. Among them, the vehicle monitoring module is used to capture the vehicle image in the preset area in real time through a deep learning algorithm to detect whether there is a vehicle entering and exiting the automobile station. The vehicle tracking module is used to track the movement path of the vehicle in the workshop based on the multi-target tracking algorithm of the neural network when there is a vehicle entering and exiting the automobile station, obtain tracking data, and record the parking time of the vehicle at each automobile station. The license plate recognition module is used to identify the license plate in the vehicle image through OCR technology to obtain the license plate information. The data transmission module is used to transmit the license plate information and tracking data to the background system. The vehicle status recording module is used to record the entry and exit time of the vehicle at the automobile station, and store the entry and exit time in the database. Through the automobile station entry and exit monitoring system provided by the present application, not only can the vehicle be automatically identified, but also the vehicle can be tracked in real time, and the license plate information can be extracted when the vehicle enters and exits, so as to realize the intelligent and automated vehicle management, improve the efficiency of vehicle management, reduce the error rate of manual operation, and is suitable for high-precision monitoring and management needs in multi-vehicle scenarios.

[0024] In order to achieve the third purpose of the present application, the technical solution of the third aspect of the present application provides a vehicle workstation entry and exit monitoring system, including: a memory and a processor, wherein the memory stores programs or instructions that can be run on the processor, and when the processor executes the program or instruction, it implements the vehicle workstation entry and exit monitoring method of any one of the technical solutions of the first aspect, and thus has the technical effect of any of the technical solutions of the first aspect mentioned above, which will not be repeated here.

[0025] In order to achieve the fourth purpose of the present application, the technical solution of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the automobile workstation entry and exit monitoring method of any one of the technical solutions of the first aspect are implemented, and thus it has the technical effect of any one of the technical solutions of the first aspect mentioned above, which will not be repeated here.

[0026] Additional aspects and advantages of the present application will become apparent in the following description or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 This is a schematic diagram of the steps of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Figure 2 This is a schematic diagram of the steps of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Figure 3 This is a schematic diagram of the steps of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Figure 4 This is a schematic diagram of the steps of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Figure 5 This is a schematic diagram of the steps of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Figure 6 This is a schematic diagram of the steps of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Figure 7 This is a schematic block diagram of the structure of a vehicle workstation entry and exit monitoring system according to an embodiment of the present application; Figure 8 This is a schematic block diagram of the structure of a vehicle workstation entry and exit monitoring system according to an embodiment of the present application; Fig. 9 This is a schematic block diagram of the structure of a vehicle workstation entry and exit monitoring system according to an embodiment of the present application; Fig.10 This is a schematic block diagram of the structure of a vehicle workstation entry and exit monitoring system according to an embodiment of the present application; Fig.11 A schematic diagram of a process flow of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application; Fig.12 This is a working principle diagram of a vehicle status recording module of a method for monitoring entry and exit of an automobile workstation according to an embodiment of the present application.

[0028] in, Figures 7 to 12 The corresponding relationship between the reference numerals and component names in the figure is: 10: vehicle workstation entry and exit monitoring system; 110: vehicle monitoring module; 120: vehicle tracking module; 130: license plate recognition module; 140: data transmission module; 150: vehicle status recording module; 160: warning notification module; 20: vehicle workstation entry and exit monitoring system; 300: memory; 400: processor. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0031] Refer to the following Figures 1 to 12 Describe the vehicle workstation entry and exit monitoring method and system and readable storage medium of some embodiments of the present application.

[0032] like Figure 1 As shown, the embodiment of the first aspect of the present application provides a method for monitoring entry and exit of an automobile workstation, comprising the following steps: Step S102: using a deep learning algorithm to capture vehicle images in a preset area in real time and detect whether there are vehicles entering or exiting the vehicle workstation; Step S104: When a vehicle enters or leaves a car workstation, a multi-target tracking algorithm based on a neural network tracks the movement path of the vehicle in the workshop, obtains tracking data, and records the parking time of the vehicle at each car workstation; Step S106: Recognize the license plate in the vehicle image by OCR technology to obtain the license plate information; Step S108: transmitting the license plate information and tracking data to the backend system; Step S110: Record the entry time and exit time of the vehicle at the vehicle workstation, and store the entry time and exit time in a database.

[0033] According to the method for monitoring the entry and exit of a car workstation provided in this embodiment, first, a deep learning algorithm is used to capture the vehicle image in a preset area in real time to detect whether there is a vehicle entering or exiting the car workstation. The deep learning model is used to analyze the vehicle image in real time, which can filter background interference and ensure the accuracy of detection and tracking. When a vehicle enters or exits the car workstation, a multi-target tracking algorithm based on a neural network is used to track the movement path of the vehicle in the workshop, obtain tracking data, and record the parking time of the vehicle at each car workstation. The multi-target tracking algorithm based on a neural network can monitor each vehicle entering the monitoring area in real time and continuously record its position and movement path. Then, the license plate in the vehicle image is recognized by OCR (Optical Character Recognition) technology to obtain the license plate information. OCR technology can process license plate images under different angles and lighting conditions, and support license plate recognition in multiple languages ​​and character formats, ensuring that the license plate information can still be efficiently recognized in complex environments. Then the license plate information and tracking data are transmitted to the background system to ensure the real-time and accuracy of the data. Finally, the entry and exit time of the vehicle at the car workstation is recorded, and the entry and exit time are stored in the database for later query and management. The automobile workstation entry and exit monitoring method provided in this application can not only automatically identify vehicles, but also track vehicles in real time and extract license plate information when vehicles enter and exit, thereby realizing intelligent and automated vehicle management, improving the efficiency of vehicle management, and reducing the error rate of manual operations. It is suitable for high-precision monitoring and management needs in multi-vehicle scenarios.

[0034] like Figure 2 As shown, according to an embodiment of the present application, the vehicle workstation entry and exit monitoring method further includes the following steps: Step S202: When the license plate recognition fails or the data transmission is abnormal, a warning notification is sent to the administrator.

[0035] In this embodiment, the vehicle station entry and exit monitoring method further includes sending a warning notification to an administrator when license plate recognition fails or data transmission is abnormal, thereby ensuring that the administrator can handle the abnormal situation in a timely manner and improving the reliability of the system. The warning notification is notified by email, text message or instant messaging tool to remind the administrator in time.

[0036] like Figure 3 As shown, according to an embodiment of the present application, a method for monitoring vehicle entry and exit in a vehicle workstation is provided, which uses a deep learning algorithm to capture vehicle images in a preset area in real time and detect whether there are vehicles entering and exiting the vehicle workstation, specifically including the following steps: Step S302: acquiring a vehicle image within a preset area through an image sensor; Step S304: Detect the vehicles in the vehicle image using a pre-trained neural network model to determine whether there are vehicles entering or exiting the vehicle workstation.

[0037] In this embodiment, a deep learning algorithm is used to capture vehicle images in a preset area in real time to detect whether there are vehicles entering or leaving the car station. Specifically, a camera or other image sensor is used to obtain vehicle images in the monitoring area, and a pre-trained neural network model is used to detect vehicles in the vehicle images to determine whether there are vehicles entering or leaving the car station. Using a neural network-based object detection algorithm, after a large number of image training, various vehicles can be accurately detected, so that license plates can be efficiently recognized in complex environments, improving recognition accuracy and robustness.

[0038] like Figure 4 As shown, according to an embodiment of the present application, a method for monitoring the entry and exit of a vehicle workstation, the license plate information and tracking data are transmitted to a background system, including the following steps: Step S402: The license plate information and tracking data are transmitted to a remote server via a network, wherein the transmission method includes asynchronous data transmission and multi-threaded processing.

[0039] In this embodiment, the license plate information and tracking data are transmitted to the background system, specifically, the license plate information and tracking data are transmitted to the remote server through the network. The transmission method includes asynchronous data transmission and multi-threaded processing, which can ensure the real-time and integrity of the data in high-concurrency scenarios.

[0040] like Figure 5 As shown, according to an embodiment of the automobile workstation entry and exit monitoring method proposed in the present application, the license plate in the vehicle image is recognized by OCR technology to obtain the license plate information, which specifically includes the following steps: Step S502: pre-processing the license plate images at different angles and under different lighting conditions; Step S504: recognizing the license plate in the preprocessed vehicle image through the OCR model to obtain the license plate information; Step S506: Associating the license plate information with the tracking data.

[0041] In this embodiment, the license plate in the vehicle image is recognized by OCR technology to obtain the license plate information. Specifically, the license plate images under different angles and different lighting conditions are first preprocessed to improve the success rate of OCR recognition. Then, the license plate in the preprocessed vehicle image is recognized by the OCR model to obtain the license plate information. Finally, the license plate information is associated with the tracking data. By recognizing the license plate in the vehicle image by OCR technology, the license plate information can still be efficiently recognized in a complex environment.

[0042] like Figure 6As shown, according to an embodiment of the present application, the vehicle workstation entry and exit monitoring method further includes the following steps: Step S602: query and export the license plate information and tracking data through the query interface.

[0043] In this embodiment, the vehicle station entry and exit monitoring method further includes querying and exporting the license plate information and tracking data through a query interface. Through the query interface, the management personnel can view the vehicle entry and exit records at any time, and the system supports data export function, which is convenient for subsequent statistics and analysis.

[0044] like Figure 7 As shown, an embodiment of the second aspect of the present application provides a system 10 for monitoring entry and exit of an automobile workstation, including: a vehicle monitoring module 110, which is used to capture vehicle images in a preset area in real time through a deep learning algorithm to detect whether there is a vehicle entering or exiting the automobile workstation; a vehicle tracking module 120, which is used to track the movement path of the vehicle in the workshop based on a multi-target tracking algorithm of a neural network when a vehicle enters or exits the automobile workstation, obtain tracking data, and record the parking time of the vehicle at each automobile workstation; a license plate recognition module 130, which is used to recognize the license plate in the vehicle image through OCR technology to obtain license plate information; a data transmission module 140, which is used to transmit the license plate information and tracking data to a background system; a vehicle status recording module 150, which is used to record the entry time and exit time of the vehicle at the automobile workstation, and store the entry time and exit time in a database.

[0045] The vehicle station entry and exit monitoring system 10 provided in this embodiment includes a vehicle monitoring module 110, a vehicle tracking module 120, a license plate recognition module 130, a data transmission module 140 and a vehicle status recording module 150. Among them, the vehicle monitoring module 110 is used to capture the vehicle image in the preset area in real time through a deep learning algorithm to detect whether there is a vehicle entering and exiting the vehicle station. The vehicle tracking module 120 is used to track the movement path of the vehicle in the workshop based on a multi-target tracking algorithm of a neural network when a vehicle enters and exits the vehicle station, obtain tracking data, and record the parking time of the vehicle at each vehicle station. The license plate recognition module 130 is used to recognize the license plate in the vehicle image through OCR technology to obtain the license plate information. The data transmission module 140 is used to transmit the license plate information and tracking data to the background system. The vehicle status recording module 150 is used to record the entry and exit time of the vehicle at the vehicle station, and store the entry and exit time in the database. The automobile workstation entry and exit monitoring system 10 provided in the present application can not only automatically identify vehicles, but also track vehicles in real time and extract license plate information when vehicles enter and exit, thereby realizing intelligent and automated vehicle management, improving the efficiency of vehicle management, and reducing the error rate of manual operations. It is suitable for high-precision monitoring and management needs in multi-vehicle scenarios.

[0046] like Figure 8 As shown, in some embodiments, optionally, the automobile workstation entry and exit monitoring system 10 also includes a warning notification module 160, and the warning notification module 160 is used to send a warning notification to the administrator when the license plate recognition fails or the data transmission is abnormal.

[0047] like Fig. 9 As shown, an embodiment of the third aspect of the present application provides an automobile workstation entry and exit monitoring system 20, including: a memory 300 and a processor 400, wherein the memory 300 stores programs or instructions that can be run on the processor 400, and when the processor 400 executes the program or instruction, it implements the steps of the automobile workstation entry and exit monitoring method of any one of the embodiments of the first aspect, and thus has the technical effect of any one of the embodiments of the first aspect mentioned above, which will not be repeated here.

[0048] The embodiment of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the automobile workstation entry and exit monitoring method of any one of the embodiments of the first aspect are implemented, and thus it has the technical effect of any one of the embodiments of the first aspect mentioned above, which will not be repeated here.

[0049] like Figures 10 to 12 As shown, according to a specific embodiment of the automobile workstation entry and exit monitoring method provided by the present application, a neural network-based object detection algorithm is used, and after a large number of image training, various vehicles can be accurately detected. The DeepSort (Deep Simple Online and Realtime Tracking, multi-target tracking based on deep learning) multi-object tracking algorithm is used to determine whether the vehicle enters or leaves the workstation based on the trajectory of the object's movement. Using a neural network-based object detection algorithm and an OCR algorithm, the position of the domestic license plate is first located, and then the characters of the license plate are extracted through OCR. By combining the real-time tracking algorithm of the neural network and the automatic license plate recognition function, not only the efficiency of vehicle management is improved, but also the error rate of manual operation is reduced. It is suitable for high-precision monitoring and management needs in multi-vehicle scenarios, and can be widely used in smart workshops, parking lots and other places that require precise vehicle monitoring.

[0050] The vehicle entry and exit monitoring method includes the following process: when the vehicle enters or leaves, the vehicle monitoring module is used to detect and track the object and capture the vehicle image. The license plate recognition module is used to perform OCR processing on the captured vehicle image to extract the license plate information. The extracted license plate information is sent to the background system through the data transmission module to record the vehicle entry and exit. The tracking module is used to record the movement trajectory of the vehicle in the workshop and save the relevant data.

[0051] Specifically, the vehicle monitoring module is used to monitor the entry and exit of vehicles in the workshop maintenance station, capture the image of the vehicle in real time and perform object detection. The tracking module is used to track the movement path of the vehicle in the workshop and record the parking time of each station. The license plate recognition module uses OCR technology to extract the license plate information from the detected vehicle image. The data transmission module sends the recognized license plate information to the background system to record the entry and exit of the vehicle. The vehicle status recording module records the entry and exit time of the vehicle and stores this information in the database. The warning notification module sends a warning notification to the administrator in the case of non-recognition or recognition failure. The vehicle monitoring module uses a deep learning algorithm to improve the accuracy and speed of detection. The license plate recognition module uses a pre-trained OCR model and supports license plate recognition in multiple languages ​​and character formats. The data transmission module can send data to a remote server through the network and supports asynchronous data transmission and multi-threaded processing. The vehicle status recording module supports the query and export of vehicle data and provides an API (Application Programming Interface) interface for other systems to call. The warning notification module supports multiple notification methods, including email, SMS or instant messaging tools, so as to remind the administrator in time. The tracking module uses real-time data analysis technology to record every movement of the vehicle in the workshop and can generate historical tracking records. The vehicle monitoring module uses a deep learning model to analyze vehicle images in real time and filter background interference to ensure the accuracy of detection and tracking. The license plate recognition module pre-processes license plate images under different angles and lighting conditions to improve the success rate of OCR recognition and associates the recognition results with the vehicle's tracking information.

[0052] In this embodiment, the vehicle monitoring module uses a deep learning algorithm to achieve efficient detection of vehicles, and extracts license plate information in combination with the license plate recognition module. The data transmission module links the recognized license plate information with the vehicle status recording module, and automatically sends entry and exit records to the background system to ensure the real-time and accuracy of the data. At the same time, the design of the warning notification module can remind the administrator when the recognition fails, ensuring the reliability of the system.

[0053] like Fig.10 As shown, the vehicle workstation entry and exit monitoring system includes a vehicle monitoring module 110 , a vehicle tracking module 120 , a license plate recognition module 130 , a data transmission module 140 , a vehicle status recording module 150 and a warning notification module 160 .

[0054] The vehicle monitoring module 110 uses a deep learning algorithm to capture vehicle images in a workshop or parking lot in real time and detect the entry or exit of vehicles. The module can obtain vehicle images in the monitoring area through a camera or other image sensor, and use a pre-trained neural network model to detect vehicles in the image.

[0055] Vehicle tracking module 120: After the vehicle monitoring module completes the initial detection, the tracking module will take over the task of tracking the vehicle's movement trajectory. This module is based on a multi-target tracking algorithm based on a neural network, which can monitor every vehicle entering the monitoring area in real time and continuously record its position and movement path. This function is particularly important when vehicles frequently enter and exit or when the vehicle is moving, ensuring that the system can accurately track and record the behavior of each vehicle.

[0056] License plate recognition module 130: After the vehicle tracking module confirms the vehicle position, the license plate recognition module is activated to recognize the license plate in the vehicle image through OCR technology. This module can process license plate images under different angles and lighting conditions, and supports license plate recognition in multiple languages ​​and character formats, ensuring that license plate information can still be efficiently recognized in complex environments.

[0057] Data transmission module 140: After the license plate recognition is completed, the data transmission module transmits the recognized license plate information and the vehicle tracking data to the background system. This module supports asynchronous data transmission and multi-threaded processing to ensure the real-time and integrity of data in high-concurrency scenarios.

[0058] Vehicle status recording module 150: Fig.12 As shown in the figure, this module is responsible for recording the entry and exit time of vehicles and storing these data in the database. Through the query interface, managers can view the entry and exit records of vehicles at any time, and the system supports data export function to facilitate subsequent statistics and analysis.

[0059] Warning notification module 160: When license plate recognition fails or the system detects data transmission anomalies, the warning notification module will automatically send a warning notification to the administrator. The module supports multiple notification methods, including email, SMS or instant messaging tools, to ensure that the administrator can handle abnormal situations in a timely manner and improve the reliability of the system.

[0060] like Fig.11As shown, specifically, the process principle of the vehicle workstation entry and exit monitoring method is as follows: Step 1: Vehicle monitoring and image capture: When a vehicle enters or leaves the monitoring area, the vehicle monitoring module is started, the vehicle image is captured in real time and the vehicle entry or exit is detected. Step 2: Vehicle tracking: After the vehicle monitoring module completes the preliminary detection, the vehicle tracking module is started to continuously track the vehicle and record the vehicle's movement trajectory and position changes. Step 3: License plate recognition: During the vehicle tracking process, the license plate recognition module performs OCR processing on the license plate in the vehicle image and extracts the license plate information. Step 4: Data transmission: After the license plate recognition is completed, the data transmission module sends the license plate information and tracking data to the background system to record the vehicle's entry and exit. Step 5: Vehicle status recording: The vehicle status recording module stores the vehicle's entry and exit time and saves these data in the database. Step 6: Abnormal notification: If the license plate recognition fails or the data transmission is abnormal, the warning notification module will send a notification to the administrator in time to ensure the security and reliability of the system.

[0061] Through the above technical solution, this embodiment can efficiently and reliably perform vehicle monitoring and license plate recognition, thereby realizing automated management of vehicles, reducing manual intervention, and improving work efficiency.

[0062] In summary, the beneficial effects of the embodiments of the present application are: 1. The neural network model trained by the deep learning algorithm can efficiently recognize license plates in complex environments and improve recognition accuracy and robustness.

[0063] 2. It integrates real-time video processing technology, which can instantly identify and record license plate information when the vehicle enters the workstation, avoiding data lag problems caused by delays in traditional systems.

[0064] 3. It has the function of automatic recording and classification, and stores the recognized license plate information and entry and exit time synchronously, which is convenient for later query and management.

[0065] 4. Combining the real-time tracking algorithm of the neural network and the automatic license plate recognition function, it not only improves the efficiency of vehicle management, but also reduces the error rate of manual operation. It is suitable for high-precision monitoring and management needs in multi-vehicle scenarios.

[0066] In this application, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise expressly defined. Terms such as "installed", "connected", "connected", and "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0067] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or module referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, should not be understood as a limitation on the present application.

[0068] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0069] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for monitoring the entry and exit of an automobile workstation, characterized in that: include: The deep learning algorithm is used to capture vehicle images in the preset area in real time to detect whether there are vehicles entering or leaving the car station; When a vehicle enters or leaves a car workstation, a multi-target tracking algorithm based on a neural network tracks the movement path of the vehicle in the workshop, obtains tracking data, and records the parking time of the vehicle at each car workstation; Recognize the license plate in the vehicle image by using OCR technology to obtain the license plate information; Transmitting the license plate information and the tracking data to a backend system; The entry time and the exit time of the vehicle at the vehicle workstation are recorded, and the entry time and the exit time are stored in a database.

2. The method for monitoring the entry and exit of an automobile workstation according to claim 1, characterized in that: Also includes: When license plate recognition fails or data transmission is abnormal, a warning notification is sent to the administrator.

3. The method for monitoring the entry and exit of an automobile workstation according to claim 1, characterized in that: The method of capturing vehicle images in a preset area in real time through a deep learning algorithm and detecting whether a vehicle enters or exits a vehicle workstation includes: Acquire vehicle images within a preset area through an image sensor; The vehicle in the vehicle image is detected by a pre-trained neural network model to determine whether there is a vehicle entering or exiting the vehicle workstation.

4. The method for monitoring the entry and exit of an automobile workstation according to claim 1, characterized in that: The transmitting the license plate information and the tracking data to the background system includes: The license plate information and the tracking data are transmitted to a remote server via a network, and a transmission method includes asynchronous data transmission and multi-thread processing.

5. The method for monitoring the entry and exit of an automobile workstation according to claim 1, characterized in that: The method of recognizing the license plate in the vehicle image by using the OCR technology to obtain the license plate information includes: Preprocess license plate images at different angles and lighting conditions; The license plate in the preprocessed vehicle image is recognized through the OCR model to obtain the license plate information; The license plate information is associated with the tracking data.

6. The method for monitoring entry and exit of an automobile workstation according to any one of claims 1 to 5, characterized in that: Also includes: The license plate information and the tracking data are queried and exported through a query interface.

7. The method for monitoring the entry and exit of an automobile workstation according to claim 2, characterized in that: The warning notification may be sent via email, text message or instant messaging tool.

8. An automobile workstation entry and exit monitoring system, characterized in that: include: A vehicle monitoring module (110) is used to capture vehicle images in a preset area in real time through a deep learning algorithm to detect whether a vehicle enters or exits a vehicle workstation; A vehicle tracking module (120) is used to track the movement path of the vehicle in the workshop based on a multi-target tracking algorithm of a neural network when a vehicle enters or exits a vehicle workstation, obtain tracking data, and record the parking time of the vehicle at each vehicle workstation; A license plate recognition module (130) is used to recognize the license plate in the vehicle image by using OCR technology to obtain license plate information; A data transmission module (140), used for transmitting the license plate information and the tracking data to a background system; The vehicle status recording module (150) is used to record the entry time and exit time of the vehicle at the vehicle workstation, and store the entry time and exit time in a database.

9. An automobile workstation entry and exit monitoring system, characterized in that: include: A memory (300) and a processor (400), wherein the memory (300) stores a program or instruction that can be run on the processor (400), and when the processor (400) executes the program or the instruction, the steps of the vehicle workstation entry and exit monitoring method as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or the instruction is executed by the processor, the steps of the vehicle workstation entry and exit monitoring method as described in any one of claims 1 to 7 are implemented.

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