Detection and management method and device for vehicle violation, and medium

Abstract

The invention discloses a detection and management method and device for vehicle violation, and a medium, and relates to the technical field of vehicle overload control. The method comprises the following steps: acquiring an image, weight and speed of a vehicle through the camera module and the weighing module; the data exchange module obtains brand information, an actual external image and load information of the vehicle according to the license plate number; the image processing module is used for preprocessing the image to obtain a deblurred image, comparing brand information, an actual external image and load information with the deblurred image and the weight of the vehicle, and judging whether the vehicle is overloaded or overloaded and whether the vehicle is refitted or not; under the condition of overload, a driver is informed in real time through the communication module, and overload and superior platform software are treated. According to the method, the road safety is improved, the traffic order is maintained, a plurality of links or departments are closely combined, and delay and errors of information transmission are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle overload control, and in particular to a detection and management method, device and medium for vehicle violations. Background Art

[0002] The load of over-limit and overloaded vehicles far exceeds the design load of roads and bridges, resulting in road damage and bridge fractures. This damage is usually invisible, but over time, the damage to the road will be exposed, greatly shortening its service life. Manual weighing or simple instrument detection has limitations in efficiency, accuracy and coverage, as well as other violations of vehicles, such as the lack of investigation and punishment of illegal behaviors such as fake license plates and overcrowded vehicles. It is impossible to detect the geometric parameters and vehicle identity information of the vehicle, and it is impossible to accurately know the overcrowding and large-scale modified vehicles. The lack of this information plays an important role in traffic safety and operation, so the function and parameter limitations of the equipment may affect the comprehensiveness and effectiveness of overload control work.

[0003] Through the above analysis, the problems and defects of the prior art are as follows:

[0004] The existing technology for detecting vehicle violations such as the three violations, duplicate license plates, and vehicle modifications has limitations in terms of efficiency, accuracy, and coverage. Summary of the invention

[0005] The embodiments of the present application provide a method, device and medium for detecting and managing vehicle violations, which can solve the problems in the prior art of limitations in efficiency, accuracy and coverage of violations such as vehicle violations, license plate duplication and vehicle modification.

[0006] In the first aspect, an embodiment of the present application provides a detection and management method for vehicle violations, which is applied to a violation monitoring system, wherein the violation monitoring system includes a camera module, a weighing module, a data exchange module, an image processing module and a communication module, and the method includes: obtaining an image, weight and speed of the vehicle through a camera module and a weighing module, the image including external image information of the vehicle and information on the number of internal personnel, and the external image information including a license plate number of the vehicle; the data exchange module obtains the brand information, actual external image and load information of the vehicle according to the license plate number; the image is pre-processed through the image processing module to obtain a deblurred image, and the brand information, actual external image and load information are compared with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited and whether the vehicle is modified; in the case of overload, the driver, the traffic control station and the upper platform software are notified in real time through the communication module.

[0007] In one implementation of the present application, the camera module includes a laser detection unit, a laser modeling unit and an image processing unit, and the laser detection unit includes lenses of a single laser camera, a dual laser camera and a triple laser camera; before obtaining the image, weight and speed of the vehicle through the camera module and the weighing module, the method also includes: installing lenses of a single laser camera, a dual laser camera and a triple laser camera on the road, and installing lenses of a dual laser camera or a triple laser camera before a preset distance of a restricted road, and the restricted roads include bridges and tunnels; obtaining restriction information of the restricted roads, and the restriction information includes length, width and height data of the tunnel and the maximum load that the bridge can bear.

[0008] In one implementation of the present application, an image of a vehicle is obtained through a camera module, specifically including: obtaining a first vehicle image of the vehicle in a single lane or multiple lanes through lens detection by a dual laser machine position or a triple laser machine position; scanning the vehicle's external contour through a laser detection unit, and establishing a 3D point cloud image of the vehicle through a laser modeling unit; combining the 3D point cloud image and the first vehicle image to obtain a second vehicle image, the second vehicle image including length, width, and height data of the current vehicle; and processing the second vehicle image through an image processing unit to obtain a third vehicle image.

[0009] In one implementation of the present application, the second vehicle image is processed by an image processing unit, specifically including: calculating the second vehicle image by a target detection algorithm to obtain the number information of internal personnel; extracting the second vehicle image by an HSV space color recognition algorithm to obtain the external image information of the vehicle.

[0010] In one implementation of the present application, the brand information, the actual external image and the deblurred image of the vehicle are compared to determine whether the vehicle has been modified, specifically including: performing image processing through a depth algorithm based on the brand information, the actual external image and the deblurred image to determine whether the color of the vehicle and the shape of the wheel hub have changed; in the case of a color change, obtaining an actual color threshold based on the actual external image; according to the actual color threshold, dividing the pixels in the deblurred image into an area that meets the threshold and an area that does not meet the threshold, and calculating the area of ​​the area that does not meet the threshold to obtain a color modification area; in the case where the modification area exceeds a preset threshold or the shape of the wheel hub has changed, notifying the superior platform software.

[0011] In one implementation of the present application, the brand information, actual external image and load information are compared with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded or overlimit, specifically including: comparing and calculating the length, width and height data and the maximum load with the vehicle information to determine whether it is overlimit or overloaded; in the case of overlimit or overload, the driver, the traffic control station and the upper-level platform software are notified in real time and an early warning is issued.

[0012] In one implementation of the present application, the method also includes: comparing the number of internal personnel information based on the vehicle's brand information and load information to determine whether the vehicle is overcrowded; in the event of overcrowding, notifying the driver, the traffic control station and the upper-level platform software in real time.

[0013] In one implementation of the present application, the method also includes: when the same vehicle appears multiple times within a preset time, obtaining the brand information and actual external image of the vehicle, and storing the vehicle separately; if the brand information or the actual external image cannot correspond, notifying the upper-level platform software in real time.

[0014] On the second aspect, the embodiment of the present application also provides a detection and management system for vehicle violations, the system including a camera module, a weighing module, a data exchange module, an image processing module and a communication module; the camera module and the weighing module are used to obtain the image, weight and speed of the vehicle, the image including the external image information of the vehicle and the number of internal personnel information, the external image information including the license plate number of the vehicle; the data exchange module is used to obtain the brand information, actual external image and load information of the vehicle according to the license plate number; the image processing module is used to pre-process the image to obtain a deblurred image, and compare the brand information, actual external image and load information with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited and whether the vehicle is modified; the communication module is used to notify the driver, the traffic control station and the upper platform software in real time in the case of overloading.

[0015] On the third aspect, the embodiments of the present application also provide a non-volatile computer storage medium for the detection and management of vehicle violations, storing computer executable instructions, and the computer executable instructions are set to: obtain the image, weight and speed of the vehicle through a camera module and a weighing module, the image including the external image information of the vehicle and the number of internal personnel information, and the external image information including the license plate number of the vehicle; the data exchange module obtains the brand information, actual external image and load information of the vehicle according to the license plate number; the image is pre-processed through the image processing module to obtain a deblurred image, and the brand information, actual external image and load information are compared with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited and whether the vehicle is modified; in the case of overload, the driver, the traffic control station and the upper platform software are notified in real time through the communication module.

[0016] The embodiments of the present application provide a method, device and medium for detecting and managing vehicle violations, which realize integrated rectification of vehicle violations, including modification, overcrowding, overloading, over-limit, etc., and can notify the driver in advance if the vehicle cannot pass through special sections such as tunnels and bridges. This can not only improve road safety and maintain traffic order, but also protect highway infrastructure, improve law enforcement efficiency, and promote public safety awareness; and integration can closely combine multiple links or departments, reduce delays and errors in information transmission, make the work process smoother, and thus improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A flow chart of a method for detecting and managing vehicle violations provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of the internal structure of a vehicle violation detection and management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0021] The embodiments of the present application provide a method, device and medium for detecting and managing vehicle violations, which solve the problem that the existing technology is limited in efficiency, accuracy and coverage of violations such as vehicle violations, license plate duplication and vehicle modification.

[0022] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0023] Figure 1 A flow chart of a method for detecting and managing vehicle violations provided in an embodiment of the present application. Figure 1 As shown, a detection and management method for vehicle violations provided in an embodiment of the present application is applied to a violation monitoring system, the violation monitoring system includes a camera module, a weighing module, a data exchange module, an image processing module and a communication module, and the method includes:

[0024] Step 10: Obtain the image, weight and speed of the vehicle through the camera module and the weighing module. The image includes the external image information of the vehicle and the number of people inside. The external image information includes the license plate number of the vehicle.

[0025] In an embodiment of the present application, the traffic violation monitoring system can integrate functions such as video management, traffic data management, weighing data management, length, width and height data management, axle data management, audio and video decoding, image processing, and network exchange.

[0026] As an optional embodiment, the camera module may include a laser detection unit, a laser modeling unit and an image processing unit, and the laser detection unit includes lenses of a single laser machine position, a dual laser machine position and a triple laser machine position.

[0027] It is understandable that before obtaining the image, weight and speed of the vehicle through the camera module and the weighing module, the method may further include:

[0028] Step 01: Install the single laser, dual laser and triple laser cameras on the road, and install the dual laser or triple laser cameras at a preset distance in front of the restricted road, which includes bridges and tunnels.

[0029] Step 02: Obtain restriction information of restricted roads, including the length, width and height data of the tunnel and the maximum load that the bridge can bear.

[0030] That is to say, on ordinary roads, only a single laser camera lens can be installed, while special sections such as some bridges and tunnels may have stricter weight limit requirements to ensure their structural safety and stability. Therefore, dual and triple laser camera lenses are installed. It is also necessary to obtain the length, width and height data of the tunnel and the maximum load that the bridge can withstand in advance.

[0031] Furthermore, obtaining an image of the vehicle through the camera module may specifically include:

[0032] Step 101: Obtain a first vehicle image of a vehicle in a single lane or multiple lanes through lens detection of a dual laser machine position or a triple laser machine position.

[0033] In this step, laser scanning technology can be used to obtain vehicle information. The lens of the dual laser camera or triple laser camera will emit a laser beam and measure the time from the emission to the reflection of the laser beam by the vehicle, so as to calculate the distance and position of the vehicle touched by the laser beam. By setting up multiple cameras, more comprehensive and accurate three-dimensional spatial data can be obtained.

[0034] Step 102: Scan the vehicle's external contour through a laser detection unit, and create a 3D point cloud image of the vehicle through a laser modeling unit.

[0035] In this step, the data obtained from the laser scanning device is usually a large amount of point cloud data. The current point cloud data is pre-processed by filtering, denoising and other pre-processing operations, and then the point cloud data is aligned to ensure that the point cloud data obtained by different camera positions can be accurately aligned to form a complete 3D point cloud image.

[0036] Step 103: Combine the 3D point cloud image and the first vehicle image to obtain a second vehicle image, where the second vehicle image includes length, width, and height data of the current vehicle.

[0037] In this step, the approximate area of ​​the vehicle is identified in the first vehicle image, and then combined with the 3D point cloud image, the vehicle area in the point cloud data is segmented through point cloud segmentation technology to extract key feature information such as surface normal vectors, curvature, edges, etc., and geometric analysis is performed to calculate the length, width and height of the vehicle.

[0038] Step 104: Process the second vehicle image by an image processing unit to obtain a third vehicle image.

[0039] Further, processing the second vehicle image by the image processing unit may specifically include:

[0040] Step 1041: Calculate the second vehicle image through a target detection algorithm to obtain information on the number of people inside.

[0041] First, you can prepare a dataset containing images of people inside vehicles, covering various scenarios such as different lighting conditions, people's postures, and occlusions. Annotate the images in the dataset, that is, mark the bounding boxes of each person in the image and the corresponding category labels, such as "person". Then, count the number of people in the image based on the detected boundary category labels.

[0042] Step 1042: extract the second vehicle image using an HSV space color recognition algorithm to obtain external image information of the vehicle.

[0043] In this step, firstly, image cropping or masking can be performed to retain only the area containing the vehicle's external information. The image is converted from RGB (Red Green Blue) color space to HSV (Hue, Saturation, Value) color space. The HSV color space is more consistent with the human eye's perception of color and has good robustness to changes in illumination. In the HSV space, the main color components of the vehicle's exterior are analyzed, and the corresponding threshold ranges of hue, saturation, and brightness are set. Each pixel in the HSV image is compared with the set color threshold. If the HSV value of the pixel falls within the set threshold range, it is regarded as part of the vehicle's exterior, otherwise it is regarded as the background or other objects. Based on the result of color segmentation, a binary mask image is created, in which the area outside the vehicle is marked as white (or 1), and the background and other objects are marked as black (or 0). The created mask is applied to the original image to extract the vehicle's external image information.

[0044] Step 20: The data exchange module obtains the brand information, actual external image and load information of the vehicle according to the license plate number.

[0045] In this step, the photo and data of the vehicle's most recent inspection can be obtained through the license plate number.

[0046] Step 30: Preprocess the image through the image processing module to obtain a deblurred image, and compare the brand information, actual external image and load information with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited, or modified.

[0047] Furthermore, comparing the brand information, the actual external image and the deblurred image of the vehicle to determine whether the vehicle is modified may specifically include:

[0048] Step 301: Perform image processing using a depth algorithm based on brand information, the actual external image, and the deblurred image to determine whether the color of the vehicle and the shape of the wheel hub have changed.

[0049] In this step, for color judgment, the color histogram, color moment and other features of the image can be extracted. For the judgment of the wheel shape, edge detection, contour extraction and other algorithms can be used to obtain the shape features of the wheel. Deep learning models (such as convolutional neural networks) can also be considered to automatically extract more complex features. The features extracted from the actual image are compared with the features extracted from the most recent inspection photo.

[0050] Step 302: when the color changes, obtain the actual color threshold according to the actual external image.

[0051] Step 303: dividing the pixels in the deblurred image into regions that meet the threshold and regions that do not meet the threshold according to the actual color threshold, and calculating the area of ​​the region that does not meet the threshold to obtain a color modification area;

[0052] In this step, each pixel in the deblurred image is compared with the color threshold, the number of pixels in the area that does not meet the threshold (i.e., the modified color area) is counted, and converted into an area in the image, which can be calculated by multiplying the number of pixels by the image resolution.

[0053] Step 304: When the modification area exceeds a preset threshold or the shape of the wheel hub changes, notify the upper-level platform software.

[0054] In this step, the upper-level platform software can be a customized traffic management system, vehicle supervision platform or part of a smart city management platform.

[0055] Furthermore, the brand information, the actual external image and the load information are compared with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded or over-limited, which may specifically include:

[0056] Step 305: Compare and calculate the length, width, height data and the maximum load with the vehicle information to determine whether it is out of limit or overloaded.

[0057] In this step, the actual detected data is compared with the vehicle's registration information to determine whether it is over-limit or overloaded.

[0058] Step 306: In case of over-limit or overload, the driver, the traffic control station and the upper-level platform software are notified in real time and an early warning is issued.

[0059] In this step, once it is determined that the vehicle is over-limit or overloaded, the system will immediately notify the driver through on-board equipment, mobile phone text messages, APP push, etc. The notification content should clearly inform the specific circumstances of the over-limit or overload, and remind the driver to take measures as soon as possible; the real-time notification and early warning mechanism can effectively prevent over-limit or overloaded vehicles from taking to the road and reduce the occurrence of traffic accidents.

[0060] Step 40: In case of overloading, the driver, the overload control station and the upper platform software are notified in real time through the communication module.

[0061] That is to say, if the vehicle exceeds the limit or is overloaded, the driver must be notified in time to avoid unnecessary danger, especially in front of tunnels and bridges, where large trucks cannot pass due to exceeding the limit, causing further traffic jams.

[0062] As another optional embodiment, the method may further include:

[0063] The number of internal personnel is compared with the vehicle's brand information and load information to determine whether the vehicle is overloaded; if overloaded, the driver, the traffic control station and the superior platform software are notified in real time.

[0064] In specific implementation, the phenomenon of overloading is often related to illegal operations, illegal modifications and other illegal acts. The rectification of vehicle overloading will help combat these illegal acts, reduce road safety hazards and improve road safety.

[0065] As another optional embodiment, the method may further include:

[0066] If the same vehicle appears multiple times within a preset time, the brand information and actual external image of the vehicle are obtained and stored separately; if the brand information or the actual external image cannot correspond, the upper-level platform software is notified in real time.

[0067] During specific implementation, if the same vehicle appears multiple times within a short period of time, we cannot give up the idea that the vehicle is a cloned vehicle. Therefore, we store it separately and then analyze it. If it cannot be matched, we notify the police immediately to ensure the safety of ourselves and others.

[0068] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a detection and management system for vehicle violations, and its structure is as follows: Figure 2 shown.

[0069] Figure 2 The internal structure diagram of a vehicle violation detection and management system provided in the embodiment of the present application is as follows. Figure 2 As shown, the system 200 includes: a camera module 201, a weighing module 202, a data exchange module 203, an image processing module 204 and a communication module 205;

[0070] In one embodiment of the present application, a camera module and a weighing module are used to obtain the image, weight and speed of the vehicle, the image including the external image information of the vehicle and the number of people inside, the external image information including the license plate number of the vehicle; a data exchange module is used to obtain the brand information, actual external image and load information of the vehicle according to the license plate number; an image processing module is used to pre-process the image to obtain a deblurred image, and compare the brand information, actual external image and load information with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited and whether the vehicle has been modified; a communication module is used to notify the driver, the traffic control station and the upper platform software in real time in the event of overloading.

[0071] Some embodiments of the present application provide corresponding Figure 1A non-volatile computer storage medium for detecting and managing vehicle violations, storing computer executable instructions, wherein the computer executable instructions are configured to: obtain the image, weight and speed of the vehicle through a camera module and a weighing module, the image including the external image information of the vehicle and the number of people inside, the external image information including the license plate number of the vehicle; obtain the brand information, actual external image and load information of the vehicle according to the license plate number through a data exchange module; pre-process the image through an image processing module to obtain a deblurred image, and compare the brand information, actual external image and load information with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited and whether the vehicle is modified; in the case of overload, notify the driver, the traffic control station and the upper platform software in real time through a communication module.

[0072] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0073] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0074] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0079] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0080] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0081] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0082] The above is only the embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for detecting and managing vehicle violations, characterized in that: Applied to a traffic violation monitoring system, the traffic violation monitoring system includes a camera module, a weighing module, a data exchange module, an image processing module and a communication module, and the method includes: The image, weight and speed of the vehicle are obtained through the camera module and the weighing module, wherein the image includes external image information of the vehicle and the number of people inside the vehicle, and the external image information includes the license plate number of the vehicle; The data exchange module obtains the brand information, actual external image and load information of the vehicle according to the license plate number; Preprocessing the image by the image processing module to obtain a deblurred image, and comparing the brand information, the actual external image and the load information with the deblurred image and the weight of the vehicle to determine whether the vehicle is overloaded, over-limited and whether the vehicle has been modified; In case of overloading, the driver, the overload control station and the upper platform software are notified in real time through the communication module.

2. A method for detecting and managing vehicle violations according to claim 1, characterized in that: The camera module includes a laser detection unit, a laser modeling unit and an image processing unit, and the laser detection unit includes lenses of a single laser position, a double laser position and a triple laser position; Before obtaining the image, weight and speed of the vehicle through the camera module and the weighing module, the method further includes: Installing a single laser camera, a dual laser camera, or a triple laser camera on a road, and installing the dual laser camera or triple laser camera lens in front of a preset distance of a restricted road, wherein the restricted road includes a bridge and a tunnel; Obtain restriction information of the restricted road, wherein the restriction information includes length, width and height data of the tunnel and the maximum load that the bridge can bear.

3. A method for detecting and managing vehicle violations according to claim 2, characterized in that: The image of the vehicle is obtained through the camera module, specifically including: Obtaining a first vehicle image of vehicles in a single lane or multiple lanes through lens detection of the dual laser machine position or the triple laser machine position; Scanning the vehicle's external contour by the laser detection unit, and establishing a 3D point cloud image of the vehicle by the laser modeling unit; Combining the 3D point cloud image and the first vehicle image to obtain a second vehicle image, wherein the second vehicle image includes length, width and height data of the current vehicle; The second vehicle image is processed by the image processing unit to obtain a third vehicle image.

4. A method for detecting and managing vehicle violations according to claim 3, characterized in that: Processing the second vehicle image by the image processing unit specifically includes: Calculating the second vehicle image by using a target detection algorithm to obtain information on the number of people inside the vehicle; The second vehicle image is extracted by using an HSV space color recognition algorithm to obtain external image information of the vehicle.

5. A method for detecting and managing vehicle violations according to claim 1, characterized in that: Comparing the brand information, the actual external image and the deblurred image of the vehicle to determine whether the vehicle is modified, specifically includes: Performing image processing by a depth algorithm according to the brand information, the actual external image and the deblurred image to determine whether the color and the shape of the wheel hub of the vehicle have changed; In the case where the color changes, obtaining an actual color threshold according to the actual external image; According to the actual color threshold, pixels in the deblurred image are divided into an area that meets the threshold and an area that does not meet the threshold, and the area of ​​the area that does not meet the threshold is calculated to obtain a color modification area; When the modified area exceeds a preset threshold or the shape of the wheel hub changes, the superior platform software is notified.

6. A method for detecting and managing vehicle violations according to claim 2, characterized in that: The brand information, actual external image and load information are compared with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded or over-limit, specifically including: Compare and calculate the length, width, height data and maximum load with the vehicle information to determine whether it is over-limit or overloaded; In case of over-limit or overload, the driver, overload control station and superior platform software will be notified in real time and an early warning will be issued.

7. A method for detecting and managing vehicle violations according to claim 1, characterized in that: The method further comprises: Comparing the number of people inside the vehicle with the brand information and load information of the vehicle to determine whether the vehicle is overloaded; In case of overcrowding, the driver, overload control station and superior platform software will be notified in real time.

8. A method for detecting and managing vehicle violations according to claim 1, characterized in that: The method further comprises: When the same vehicle appears multiple times within a preset time, obtaining the brand information and actual external image of the vehicle, and storing the vehicle separately; If the brand information or actual external image does not correspond, the upper-level platform software will be notified in real time.

9. A detection and management system for vehicle violations, characterized in that: The system includes a camera module, a weighing module, a data exchange module, an image processing module and a communication module; A camera module and a weighing module, used to obtain an image, weight and speed of the vehicle, wherein the image includes external image information of the vehicle and information on the number of people inside the vehicle, and the external image information includes the license plate number of the vehicle; A data exchange module, used to obtain the brand information, actual external image and load information of the vehicle according to the license plate number; An image processing module is used to pre-process the image to obtain a deblurred image, and compare the brand information, actual external image and load information with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited, or modified; The communication module is used to notify the driver, the overload control station and the upper-level platform software in real time in case of overload.

10. A non-volatile computer storage medium for detecting and managing vehicle violations, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Obtaining the image, weight and speed of the vehicle through the camera module and the weighing module, wherein the image includes the external image information of the vehicle and the number of people inside, and the external image information includes the license plate number of the vehicle; The data exchange module obtains the brand information, actual external image and load information of the vehicle according to the license plate number; Preprocessing the image through an image processing module to obtain a deblurred image, and comparing the brand information, actual external image and load information with the deblurred image and weight of the vehicle to determine whether the vehicle is overloaded, over-limited, or modified; In case of overloading, the driver, the overload control station and the upper-level platform software will be notified in real time through the communication module.

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