A truck axle type identification method, truck weight limit judgment method and system

Abstract

The application provides a truck axle type identification method, a truck weight limit judgment method and system, and relates to the technical field of vehicle monitoring. The method comprises the following steps: obtaining a region detection frame corresponding to a truck target and a target detection frame corresponding to an axle target in an image; mapping the region detection frame and the target detection frame in a rectangular coordinate system; determining the coordinate values of the region detection frame and the target detection frame in the rectangular coordinate system; judging the positional relationship between each target detection frame and the region detection frame according to the coordinate values; obtaining all target detection frames located in the region detection frame; sequentially sorting all target detection frames according to the horizontal coordinates; sequentially calculating the distance between adjacent target detection frames and the average width of adjacent target detection frames; if the distance is less than the average width, the detection target in the adjacent target detection frame is in a coupled axle relationship. The method is used to solve the technical problem that when the axles of a truck are detected, the axles of other nearby vehicles are also detected, which interferes with the judgment result.

Description

Technical Field

[0001] This invention relates to the field of vehicle monitoring technology, and more specifically, to a method for identifying the axle type of a truck, a method for determining the weight limit of a truck, and a system thereof. Background Technology

[0002] If we obtain the axle type of a freight truck, we can determine the truck's gross vehicle weight limit (weight limit). The axle type is determined based on the coupling of the axles, which describes the proximity of adjacent axles. Simply put, two axles close together are coupled, and vice versa. Figure 1 As shown in the picture, the truck is a four-axle vehicle. The first and second axles and the third and fourth axles are closely adjacent to each other and are connected. The second and third axles are less adjacent and are not connected.

[0003] The above analysis shows that during vehicle movement, quickly determining the truck's axle type can reveal its weight limit. When the vehicle passes a roadside weighing device, it can be determined whether it is overloaded. However, in reality, scenarios are complex, with numerous vehicles surrounding the target truck, such as... Figure 2 As shown, when the axle of a truck is being inspected, the axles of other nearby vehicles are also being detected, which interferes with the judgment results. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying truck axle type, a method and system for determining truck weight limits, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for determining the axle type of a freight truck, including:

[0006] Obtain the region detection box corresponding to the truck and the target detection box corresponding to the wheel axle in the image;

[0007] A Cartesian coordinate system is constructed based on the image, and the region detection box and the target detection box are mapped in the Cartesian coordinate system;

[0008] Determine the coordinates of the region detection box and the target detection box in a Cartesian coordinate system, and determine the positional relationship between the target detection box and the region detection box based on the coordinates.

[0009] Obtain all target detection boxes located within the region detection box, and sort all target detection boxes sequentially according to their x-coordinates;

[0010] The distance between adjacent target detection boxes and the average width of adjacent target detection boxes are calculated sequentially. If the distance is less than the average width, the wheel axles within the adjacent target detection boxes are in a coupling relationship.

[0011] Furthermore, the acquisition of the region detection box corresponding to the truck and the target detection box corresponding to the wheel axle in the image specifically includes:

[0012] Acquire an image, the image of which at least contains a truck and its axles;

[0013] The truck and wheel axle in the image are detected by the object detection algorithm, and the region detection box and object detection box are obtained respectively.

[0014] Furthermore, the step of constructing a Cartesian coordinate system based on the image and mapping the region detection box and the target detection box into the Cartesian coordinate system specifically includes:

[0015] Construct a Cartesian coordinate system by taking the top left or bottom left corner of the image as the origin;

[0016] Calculate the distances between the top edge of the region detection box and the top boundary of the image, the bottom edge and the top boundary of the image, the left edge and the left boundary of the image, and the right edge and the left boundary of the image, respectively.

[0017] Map the region detection box and the target detection box in the Cartesian coordinate system according to the distance calculated above.

[0018] Furthermore, determining the coordinates of the region detection box and the target detection box in a Cartesian coordinate system, and judging the positional relationship between the target detection box and the region detection box based on the coordinate values, specifically includes:

[0019] Get the coordinates of the top left corner of the region detection box (x) t1 ,y t1 ) and the coordinates of the lower right corner (x t2 ,y t2 );

[0020] Get the top-left corner coordinates (x, y) of the target detection box w1 ,y w1 ) and the coordinates of the lower right corner (x w2 ,y w2 );

[0021] Calculate the center point coordinates (x, y) of the target detection box using the following formula. c ,y c ):

[0022]

[0023] Determine the positional relationship between the current target detection box and the region detection box:

[0024] when When the target detection box is located within the region detection box, then the current target detection box is located within the region detection box.

[0025] Furthermore, the step of obtaining all target detection boxes located within the region detection box and sorting all target detection boxes sequentially according to the horizontal axis coordinate specifically includes:

[0026] Store all target detection boxes and their coordinate values ​​within the region detection box in a list;

[0027] The target detection boxes are sorted in ascending order according to their horizontal coordinates.

[0028] Furthermore, the step of sequentially calculating the distance between adjacent target detection boxes and the average width of adjacent target detection boxes, and if the distance is less than the average width, then the axles within the adjacent target detection boxes are in a coupling relationship, specifically includes:

[0029] Calculate the distance l between adjacent target detection boxes;

[0030] Calculate the width d = x of the target detection box. w2 -x w1 ;

[0031] Average width of two adjacent object detection boxes Wherein, d1 and d2 are the widths of two adjacent target detection boxes, respectively;

[0032] Iterate through all target detection boxes within the region detection box in sequence:

[0033] when When the time is right, the wheel axle coupling in the two adjacent target detection boxes is marked with the value 1;

[0034] Conversely, if the axles of two adjacent target detection boxes are not connected, they are marked with a value of 0.

[0035] Furthermore, it also includes:

[0036] After traversing all target detection boxes within the region detection box, the marker values ​​are sorted sequentially according to the order of the region detection boxes to obtain the wheel axle coupling marker values;

[0037] Output the wheel axle coupling mark value of the area detection frame.

[0038] Secondly, this application also provides a method for determining the weight limit of a truck, including:

[0039] Obtain the wheel axle coupling mark values ​​of the truck;

[0040] The axle type of the truck is determined based on the wheel axle marking values;

[0041] Use the overload and over-limit standard table to find the total mass limit corresponding to the shaft type.

[0042] Thirdly, this application also provides a truck axle type discrimination system, the system comprising:

[0043] Detection unit: used to acquire the region detection box corresponding to the truck in the image and the target detection box corresponding to the wheel axle;

[0044] Mapping unit: used to construct a Cartesian coordinate system based on the image, and map the region detection box and the target detection box into the Cartesian coordinate system;

[0045] Coordinate calculation unit: used to determine the coordinate values ​​of the region detection box and the target detection box in the rectangular coordinate system, and to determine the positional relationship between the target detection box and the region detection box based on the coordinate values;

[0046] Sorting Unit: Obtain all target detection boxes located within the region detection box, and sort all target detection boxes sequentially according to their horizontal coordinates;

[0047] Judgment unit: used to sequentially calculate the distance between adjacent target detection boxes and the average width of adjacent target detection boxes. If the distance is less than the average width, the wheel axles in the adjacent target detection boxes are in a coupling relationship.

[0048] Fourthly, this application also provides a weight limit determination system based on trucks, the system comprising:

[0049] Acquisition module: Acquires the wheel axle coupling values ​​of the truck;

[0050] Determine module: Determine the axle type and number of axles of the truck based on the wheel axle coupling values;

[0051] Matching module: Uses the overload and limit standard table to find the total mass limit corresponding to the shaft type.

[0052] The beneficial effects of this invention are as follows:

[0053] 1. To eliminate external interference, this invention first determines the attribution relationship between the axle and the truck. A two-dimensional coordinate system is constructed based on the image of the target truck to be detected, and the detection boxes for the truck and the axle are mapped into this system. The truck and axle are located using their coordinate positions, and the positional relationship between them is determined, thereby identifying whether the detected axle belongs to the target truck. This method effectively avoids interference from the axles of other nearby vehicles, ensuring the accuracy of axle identification and providing a guarantee for subsequent judgments.

[0054] 2. This invention calculates the distance between adjacent target detection boxes and the average width of adjacent target detection boxes, and then compares the distance and the average width to determine whether adjacent axles are connected. The results are marked with numerical values, and finally the marked values ​​are output in a unified manner to obtain the axle type of the truck.

[0055] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A schematic diagram showing wheel and axle coupling and non-coupling;

[0058] Figure 2 This is a schematic diagram illustrating a scenario of wheel and axle misjudgment in the prior art;

[0059] Figure 3 This is a schematic diagram of a truck axle type identification process as described in Example 1;

[0060] Figure 4 This is a schematic diagram illustrating the distance calculation between the region detection box and the image in Example 1;

[0061] Figure 5 This is a flowchart illustrating step S4 in Example 1;

[0062] Figure 6 A schematic diagram of the Cartesian coordinate system mapping of the target detection box. Figure 1 ;

[0063] Figure 7 A schematic diagram of the Cartesian coordinate system mapping of the target detection box. Figure 2 ;

[0064] Figure 8 This is a schematic diagram of the unit structure of a truck axle type discrimination system;

[0065] Figure 9 A schematic diagram of the module structure of a truck axle type determination system;

[0066] Figure 10This is a schematic diagram of a truck axle type identification device according to an embodiment of the present invention.

[0067] The markings in the diagram are: 800, truck axle type identification device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0069] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0070] Example 1:

[0071] See Figure 3 This embodiment provides a method for identifying the axle type of a truck, including...

[0072] S1. Obtain the region detection box corresponding to the truck and the target detection box corresponding to the wheel axle in the image:

[0073] S11. Acquire an image, the image of which at least includes the target truck and its axles; preferably, the image should be a side view of the vehicle, including all the axles of the target truck.

[0074] S12. The truck and wheel axle in the image are detected by the target detection algorithm, and the region detection box and the target detection box are obtained respectively. The target detection algorithm can be any one of YOLOv4, SSD, or Fast R-CNN. Preferably, in this embodiment, YOLOv4 algorithm is used for training to perform detection.

[0075] Based on the above embodiments, S2. Construct a Cartesian coordinate system based on the image, and map the region detection box and the target detection box into the Cartesian coordinate system:

[0076] S21. Construct a Cartesian coordinate system by taking the top left corner or bottom left corner of the image as the origin. Preferably, in this embodiment, the top left corner is used as the origin to construct the Cartesian coordinate system.

[0077] Please see Figure 4 S22. Calculate the distances between the top edge of the region detection box and the top boundary of the image (top), the bottom edge of the region detection box and the top boundary of the image (bottom), the left edge of the region detection box and the left boundary of the image (left), and the right edge of the region detection box and the left boundary of the image (right).

[0078] S23. Map the region detection box and the target detection box into the Cartesian coordinate system according to the distance calculated above. Please refer to [link to relevant documentation]. Figure 6 , Figure 7 .

[0079] Based on the above embodiments, S3. Determine the coordinate values ​​of the region detection box and the target detection box in the Cartesian coordinate system, and determine the positional relationship between the target detection box and the region detection box based on the coordinate values:

[0080] S31. Obtain the coordinates of the top left corner of the region detection box (x... t1 ,y t1 ) and the coordinates of the lower right corner (x t2 ,y t2 );

[0081] S32. Obtain the coordinates of the top left corner of the target detection box (x... w1 ,y w1 ) and the coordinates of the lower right corner (x w2 ,y w2 );

[0082] S33. Calculate the center point coordinates (x, y, y) of the target detection box using formula (1). c ,y c ):

[0083]

[0084] Determine the positional relationship between the current target detection box and the region detection box:

[0085] when If the target detection box is located within the area detection box, then the current wheel axle belongs to the truck.

[0086] Please see Figure 5 Based on the above embodiments, S4. Obtain all target detection boxes located within the region detection box, and sort all target detection boxes sequentially according to the horizontal axis coordinate:

[0087] S41. Store all target detection boxes within the region detection box and their coordinate values ​​in a list. The coordinate values ​​include at least the coordinates of the top-left corner (x, y, y). w1 ,y w1 ) and the coordinates of the lower right corner (x w2 ,y w2 );

[0088] S42. Sort the target detection boxes in ascending order according to the horizontal coordinate, such as: No. 1, No. 2, No. 3... Specifically, the horizontal coordinate can be the horizontal coordinate of the upper left corner or the horizontal coordinate of the lower right corner.

[0089] Based on the above embodiments, S5. The distance between adjacent target detection boxes and the average width of adjacent target detection boxes are calculated sequentially. If the distance is less than the average width, the axles within the adjacent target detection boxes are in a coupling relationship.

[0090] S51. Calculate the distance between adjacent target detection boxes. Among them, the These are the coordinates of the top-left corners of two adjacent target detection boxes;

[0091] S52. Calculate the width d = x of the target detection box. w2 -x w1 ;

[0092] S53. Average width of two adjacent object detection boxes Wherein, d1 and d2 are the widths of two adjacent target detection boxes, respectively;

[0093] S54. Iterate through all target detection boxes within the region detection box in sequence, for example: 1 and 2, 2 and 3, 3 and 4... and so on:

[0094] when When the time is right, the wheel axle coupling in the two adjacent target detection boxes is marked with the value 1;

[0095] Conversely, if the axles of two adjacent target detection boxes are not connected, they are marked with a value of 0.

[0096] by Figure 6 , Figure 7 For example, Figure 6 The display shows two adjacent target display boxes, A and B. Calculations show l = 0.2, d1 = 0.3, and d2 = 0.3. because Therefore, display frame A and display frame B are connected by a shaft;

[0097] Figure 7The display shows two adjacent target display boxes, C and D. Calculations show l = 0.4, d1 = 0.2, and d2 = 0.2. because Therefore, display box A and display box B are not connected.

[0098] S55. After traversing all target detection boxes within the region detection box, sort the marker values ​​sequentially according to the order of the region detection boxes to obtain the wheel axle coupling marker values, for example, 0-1-1;

[0099] S56. Output the wheel axle coupling mark value of the area detection frame.

[0100] Example 2:

[0101] This embodiment provides a method for determining the weight limit of a truck, the method including:

[0102] S6. Obtain the wheel axle coupling mark value of the truck;

[0103] S7. Determine the axle type of the truck based on the axle marking value. Specifically, use the axle marking value to find the corresponding axle type from the overload and oversize standard table. The axle types and axle marking values ​​in the table have a one-to-one correspondence.

[0104] S8. Use the overload and oversize standard table to find the total mass limit corresponding to the axle type, i.e. the truck weight limit. Please refer to Table 1.

[0105] Table 1 Comparison of Axle Types for Some Freight Trucks

[0106]

[0107]

[0108] Example 3:

[0109] Please see Figure 8 This embodiment also provides a truck axle type identification system, the system comprising:

[0110] Detection unit: used to acquire the region detection box corresponding to the truck in the image and the target detection box corresponding to the wheel axle;

[0111] Mapping unit: used to construct a Cartesian coordinate system based on the image, and map the region detection box and the target detection box into the Cartesian coordinate system;

[0112] Coordinate calculation unit: used to determine the coordinate values ​​of the region detection box and the target detection box in the rectangular coordinate system, and to determine the positional relationship between the target detection box and the region detection box based on the coordinate values;

[0113] Sorting Unit: Obtain all target detection boxes located within the region detection box, and sort all target detection boxes sequentially according to their horizontal coordinates;

[0114] Judgment unit: used to sequentially calculate the distance between adjacent target detection boxes and the average width of adjacent target detection boxes. If the distance is less than the average width, the detected targets in the adjacent target detection boxes are in a coupling relationship.

[0115] Example 4:

[0116] Please see Figure 9 This embodiment also provides a weight limit determination system based on trucks, the system comprising:

[0117] Acquisition module: Acquires the wheel axle coupling values ​​of the truck;

[0118] Determine module: Determine the axle type and number of axles of the truck based on the wheel axle coupling values;

[0119] Matching module: Uses the overload and limit standard table to find the total mass limit corresponding to the shaft type.

[0120] Example 5:

[0121] Please see Figure 10 This embodiment also provides a truck axle type identification device. The truck axle type identification device described below and the truck axle type identification method described above can be referred to in correspondence.

[0122] Figure 10 This is a block diagram illustrating a truck axle type determination device 800 according to an exemplary embodiment. For example... Figure 10 As shown, the truck axle type identification device 800 may include: a processor 801 and a memory 802. The truck axle type identification device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0123] The processor 801 controls the overall operation of the truck axle type identification device 800 to complete all or part of the steps in the truck axle type identification method described above. The memory 802 stores various types of data to support the operation of the truck axle type identification device 800. This data may include, for example, instructions for any application or method operating on the truck axle type identification device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the truck axle type determination device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0124] In an exemplary embodiment, the truck axle type determination device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the truck axle type determination method described above.

[0125] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the truck axle type determination method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the truck axle type determination device 800 to complete the truck axle type determination method described above.

[0126] Example 6:

[0127] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the truck axle type discrimination method described above.

[0128] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the truck axle type discrimination method described in the above method embodiments.

[0129] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0131] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A truck axle type discrimination method characterized by comprising: The method comprises the following steps: Obtain the region detection frame corresponding to the truck and the target detection frame corresponding to the wheel shaft in the image; Construct a planar rectangular coordinate system based on the image, and map the region detection frame and the target detection frame in the rectangular coordinate system; Determine the coordinate values of the region detection frame and the target detection frame in the rectangular coordinate system, and judge the positional relationship between the target detection frame and the region detection frame according to the coordinate values, which comprises: Obtains the left upper corner coordinate ( ) and the right lower corner coordinate ( ) of the acquisition region detection frame; Obtains the upper left corner coordinate ( ) and the lower right corner coordinate ( ) of the target detection frame. The center point coordinates of the target detection frame are calculated by using the following formula: ): ； Judging the positional relationship between the current target detection frame and the region detection frame: When the current target detection frame is located within the region detection frame; Obtain all target detection frames located in the region detection frame, and sort all target detection frames in sequence according to the horizontal coordinate; Calculate the distance between adjacent target detection frames and the average width of adjacent target detection frames in sequence, and if the distance is less than the average width, the detection targets in the adjacent target detection frames are in the shaft coupling relationship.

2. The truck axle type determination method according to claim 1, characterized in that The method for obtaining the region detection frame corresponding to the truck and the target detection frame corresponding to the wheel shaft in the image comprises: Obtain an image, wherein the image at least contains a truck and a wheel shaft; Detect the truck and the wheel shaft in the image through a target detection algorithm to obtain the region detection frame and the target detection frame respectively.

3. The truck axle type determination method of claim 1, wherein The method for constructing a planar rectangular coordinate system based on the image and mapping the region detection frame and the target detection frame in the rectangular coordinate system comprises: Take the upper left corner or the lower left corner of the image as the coordinate origin to construct a planar rectangular coordinate system; Calculate the distance between the upper edge of the region detection frame and the upper boundary of the image, the distance between the lower edge of the region detection frame and the upper boundary of the image, the distance between the left edge of the region detection frame and the left boundary of the image, and the distance between the right edge of the region detection frame and the left boundary of the image respectively; Map the region detection frame and the target detection frame in the rectangular coordinate system according to the distances calculated above.

4. The truck axle type determination method of claim 1, wherein The method for obtaining all target detection frames located in the region detection frame and sorting all target detection frames in sequence according to the horizontal coordinate comprises: Store all target detection frames and their coordinate values in the region detection frame in a list; Sort the target detection frames in ascending order according to the horizontal coordinate.

5. The truck axle type determination method of claim 2, wherein The method for calculating the distance between adjacent target detection frames and the average width of adjacent target detection frames in sequence, and if the distance is less than the average width, the wheel shafts in the adjacent target detection frames are in the shaft coupling relationship, comprises: Computing distance between adjacent target bounding boxes ; Computing a width of a target detection box ; an average width of two adjacent target detection boxes wherein the , are the widths of the two adjacent target detection boxes, respectively Iterate all target detection frames in the region detection frame in sequence: When the adjacent two target detection boxes are connected by an axle, marked as a value 1. If not, the wheel shafts of the adjacent two target detection frames are not in the shaft coupling relationship, and are marked as a numerical value 0.

6. The truck axle type determination method of claim 5, wherein The method further comprises: After iterating all target detection frames in the region detection frame, sort the marked numerical values in sequence according to the order of the region detection frame to obtain the wheel shaft coupling marking value; Output the wheel shaft coupling marking value of the region detection frame.

7. A method of judging a weight limit of a truck, comprising the truck axle type judging method according to claim 1, characterized by The method comprises: Obtain the wheel shaft coupling marking value of the truck; Determine the axle type of the truck according to the wheel shaft marking value; Look up the total mass limit value corresponding to the axle type in the overloading and overloading standard table.

8. A truck axle type discrimination system for use in the truck axle type discrimination method according to any one of claims 1 to 6, characterized by The system comprises: A detection unit for obtaining the region detection frame corresponding to the truck and the target detection frame corresponding to the wheel shaft in the image; A mapping unit for constructing a planar rectangular coordinate system based on the image, and mapping the region detection frame and the target detection frame in the rectangular coordinate system; A coordinate calculation unit for determining the coordinate values of the region detection frame and the target detection frame in the rectangular coordinate system, and judging the positional relationship between the target detection frame and the region detection frame according to the coordinate values. The sorting unit is configured to acquire all target detection frames located in the region detection frame, and sort all target detection frames according to the horizontal coordinates in sequence. The judging unit is configured to calculate the distance between adjacent target detection frames and the average width of adjacent target detection frames in sequence, and if the distance is less than the average width, the wheel shafts in the adjacent target detection frames are in the jointed shaft relationship.

9. A truck-based weight limit determination system for use in the truck-based weight limit determination method of claim 7, characterized by, The system comprises: The acquisition module is configured to acquire the wheel shaft jointed shaft value of the truck. The determination module is configured to determine the shaft type and the number of shafts of the truck according to the wheel shaft jointed shaft value. The matching module is configured to find the total mass limit value corresponding to the shaft type by using the over-limit and over-load standard table.

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