Roadbed flatness detection method, system, device and program

Abstract

The invention belongs to the technical field of machine vision detection, and particularly discloses a road subgrade flatness detection method, system, equipment and program, and the method comprises the steps: collecting a gap detection image between a road subgrade surface and a straight scale, carrying out the image cutting and conversion processing, and obtaining a gap detection segmented binary image; and then gap detection and anchoring gap region feature extraction are carried out on the gap detection segmented binary images, three-dimensional gap features of the gap detection segmented binary images are determined, a three-dimensional gap feature set of the detection image is established, the three-dimensional gap feature set is utilized to carry out neural network flatness detection, and the flatness of the detection image is detected. And determining the roadbed flatness of the corresponding straight scale road section. The gap distribution characteristics between the road subgrade surface and the straight scale can be efficiently detected through a machine vision detection technology, the flatness of the road subgrade is accurately judged through the gap distribution characteristics, and the efficiency and reliability of road subgrade flatness detection can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of machine vision detection, and particularly relates to a road subgrade flatness detection method, system, device and program. BACKGROUND

[0002] The road surface flatness is one of the main technical indexes for evaluating the road surface quality, which is related to the safety and comfort of driving, the impact force on the road surface and the service life. The uneven road surface will increase the driving resistance and cause additional vibration of the vehicle, which will cause the vehicle to shake, affect the driving speed and safety, and affect the driving stability and passenger comfort. At the same time, the vibration will also exert an impact force on the road surface, thereby accelerating the damage of the road surface and the wear of the tires, and increasing the energy consumption. In addition, the uneven road surface will also accumulate rainwater, accelerating the damage of the road surface.

[0003] The road surface flatness refers to the longitudinal concave-convex deviation of the road surface, and mainly reflects the flatness of the longitudinal section profile curve of the road surface. When the longitudinal section profile curve of the road surface is relatively smooth, it indicates that the road surface is relatively flat, or the flatness is relatively good, and vice versa. The straight ruler method is a common method for detecting the road surface flatness at present, that is, a straight ruler with a specified length is placed on the road surface, and the gap between the straight ruler and the road surface is measured manually to determine the flatness of the road surface. This flatness detection method relies on manual measurement experience, and the efficiency and reliability of the detection need to be improved. SUMMARY

[0004] The present application aims to provide a road subgrade flatness detection method, system, device and program to solve the above problems in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: In a first aspect, a road subgrade flatness detection method is provided, comprising: obtaining an initial gap detection image between the road subgrade surface and the straight ruler collected by the image collector along the length direction of the straight ruler, the initial gap detection image containing the road subgrade surface and the straight ruler; performing image rotation, scaling and cropping processing on the initial gap detection image to obtain a gap detection image with fixed direction and fixed size, and cutting the gap detection image into a plurality of gap detection sub-images based on the scale marks of the straight ruler on the gap detection image; performing gray scale processing on each gap detection sub-image to obtain a corresponding gap detection sub-smooth gray image, and performing image binarization processing on each gap detection sub-smooth gray image to obtain a corresponding gap detection sub-binary image; perform edge detection on the gap detection segmented binary image, determine each edge closed gap region in the gap detection segmented binary image, and extract a corresponding anchor gap region from each edge closed gap region; determine a vertical gap parameter and a horizontal gap parameter of each anchor gap region in the gap detection segmented binary image based on a direction perpendicular to the flat ruler as a vertical direction and a direction parallel to the flat ruler as a horizontal direction in the gap detection segmented binary image; construct a three-dimensional gap feature of the corresponding gap detection segmented binary image based on the number of anchor gap regions contained and the vertical gap parameter and the horizontal gap parameter of each anchor gap region; use the three-dimensional gap features of each gap detection segmented binary image to form a three-dimensional gap feature set, input the three-dimensional gap feature set into a preset flatness detection model for flatness detection, obtain a flatness detection result of the corresponding road subgrade section under the flat ruler, and output the flatness detection result.

[0006] In one possible design, the gray processing on the gap detection segmented images to obtain corresponding gap detection segmented smooth gray images, and the image binarization processing on the gap detection segmented smooth gray images to obtain corresponding gap detection segmented binary images, include: substitute three color channel component values R, G and B of each pixel point of the gap detection segmented image into a gray conversion formula H=0.39×R+0.50×G+0.11×B for calculation to obtain a gray value H of each pixel point, and construct a gap detection segmented gray image based on the gray value H of each pixel point; perform median filtering processing on the gap detection segmented gray image to obtain a gap detection segmented smooth gray image; perform image binarization processing on the gap detection segmented smooth gray image by using the maximum inter-class variance method to obtain a gap detection segmented binary image.

[0007] In one possible design, the edge detection on the gap detection segmented binary image to determine each edge closed gap region in the gap detection segmented binary image includes: perform edge detection on the gap detection segmented binary image by using a Canny edge detection operator to determine each edge closed gap region in the gap detection segmented binary image.

[0008] In one possible design, the extraction of a corresponding anchor gap region from each edge closed gap region includes: determine the number of pixel points contained in each pixel column in the edge closed gap region, and use the number of pixel points as an anchor value of the corresponding pixel column; The region composed of the pixel columns in which the continuous multiple anchor values in the edge closure gap region are all greater than the set reference threshold is taken as the anchor gap region.

[0009] In a possible design, the determining of the vertical gap parameter and the horizontal gap parameter of each anchor gap region in the gap detection segmented binary image comprises: determining the maximum image interval of the anchor gap region in the vertical direction, and taking the maximum image interval of the anchor gap region in the vertical direction as the vertical gap parameter of the anchor gap region; determining the maximum image interval of the anchor gap region in the horizontal direction, and taking the maximum image interval of the anchor gap region in the horizontal direction as the horizontal gap parameter of the anchor gap region.

[0010] In a possible design, the constructing of the three-dimensional gap feature corresponding to the gap detection segmented binary image based on the number of the anchor gap regions contained and the vertical gap parameter and the horizontal gap parameter of each anchor gap region comprises: determining the number L of the anchor gap regions contained in the gap detection segmented binary image, determining the maximum value M of the vertical gap parameters of each anchor gap region in the gap detection segmented binary image, and determining the maximum value N of the horizontal gap parameters of each anchor gap region in the gap detection segmented binary image; constructing the three-dimensional gap feature (L, M, N) of the gap detection segmented binary image by using L, M and N.

[0011] In a possible design, before inputting the three-dimensional gap feature set into the preset flatness detection model to perform flatness detection, the method further comprises: constructing an initialized BP neural network model, and performing training optimization on the initialized BP neural network model by using a training set until a set training condition is met, to obtain a trained flatness detection model, the training set containing a plurality of three-dimensional gap feature set samples, and each three-dimensional gap feature set sample being labeled with a corresponding flatness label.

[0012] In a second aspect, a road subgrade flatness detection system is provided, comprising an image acquisition unit, an image segmentation unit, an image conversion unit, a gap detection unit, a parameter determination unit, a feature construction unit and a flatness detection unit, wherein: The image acquisition unit is configured to acquire an initial gap detection image between a road subgrade surface and a straight ruler, which is collected by an image collector along the length direction of the straight ruler, and the initial gap detection image contains the road subgrade surface and the straight ruler. an image segmentation unit configured to perform image rotation, scaling and clipping on the initial gap detection image to obtain a gap detection image with a fixed direction and a fixed size, and segment the gap detection image into a plurality of gap detection sub-images based on scale marks on the straight ruler in the gap detection image; an image conversion unit configured to perform grayscale processing on each of the gap detection sub-images to obtain a corresponding gap detection sub-smooth grayscale image, and perform image binarization processing on each of the gap detection sub-smooth grayscale images to obtain a corresponding gap detection sub-binary image; a gap detection unit configured to perform edge detection on each of the gap detection sub-binary images to determine each edge-closed gap region in the gap detection sub-binary image, and extract a corresponding anchor gap region from each edge-closed gap region; a parameter determination unit configured to determine a vertical direction as a direction perpendicular to the straight ruler and a horizontal direction as a direction parallel to the straight ruler in each of the gap detection sub-binary images, and determine a vertical gap parameter and a horizontal gap parameter of each anchor gap region in the gap detection sub-binary image; a feature construction unit configured to construct a three-dimensional gap feature of each of the gap detection sub-binary images based on a number of anchor gap regions contained in the gap detection sub-binary image and the vertical gap parameter and the horizontal gap parameter of each anchor gap region; a flatness detection unit configured to use the three-dimensional gap features of each of the gap detection sub-binary images to form a three-dimensional gap feature set, input the three-dimensional gap feature set into a preset flatness detection model to perform flatness detection, obtain a flatness detection result of a corresponding roadbed section under the straight ruler, and output the flatness detection result.

[0013] In a third aspect, a roadbed flatness detection device is provided, including: a memory configured to store instructions; a processor configured to read the instructions stored in the memory and execute the method of any one of the first aspect according to the instructions.

[0014] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the method of any one of the first aspect. Meanwhile, a computer program is also provided, and when the computer program is executed on a computer, the computer program executes the method of any one of the first aspect.

[0015] Beneficial effects: the present application obtains the gap detection segmented binary image through image cropping and conversion processing of the gap detection image between the road subgrade surface and the flat ruler, then carries out gap detection and anchor gap region feature extraction on the gap detection segmented binary image, determines the three-dimensional gap features of each gap detection segmented binary image, and then assembles the three-dimensional gap feature set of the detection image, and then uses the three-dimensional gap feature set for neural network flatness detection, determines the subgrade flatness of the corresponding flat ruler section, and can realize rapid and intelligent road subgrade flatness detection. The machine vision detection technology can efficiently detect the gap distribution features between the road subgrade surface and the flat ruler, and then accurately determine the flatness of the road subgrade by using the gap distribution features, which can replace the traditional manual length straight ruler detection method, and improve the efficiency and reliability of the road subgrade flatness detection. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1 The schematic diagram of the steps of the method in embodiment 1 of the present application is shown in the figure. Figure 2 The schematic diagram of the system in embodiment 2 of the present application is shown in the figure. Figure 3 The schematic diagram of the system in embodiment 2 of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] It should be noted that the description of these embodiment modes is used to help understand the present application, but does not constitute a limitation on the present application. The specific structure and functional details disclosed herein are only used to describe the example embodiments of the present application. However, the present application can be embodied in many alternative forms, and should not be understood as being limited in the embodiments set forth herein.

[0019] It should be understood that, unless otherwise explicitly specified and limited, the corresponding terms should be broadly understood, for example, "connection" can be fixed connection, can also be detachable connection, or integral connection; can be electrical connection, can be direct connection, can also be indirect connection through intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments can be understood according to the specific circumstances.

[0020] In the following description, specific details are provided to facilitate a complete understanding of the example embodiments. However, a person of ordinary skill in the art will understand that the example embodiments can be practiced without these specific details. For example, devices can be shown in block diagram form to avoid obscuring examples. In other examples, well-known processes, structures, and techniques can not be shown in detail to avoid obscuring the embodiments.

[0021] Embodiment 1 This embodiment provides a road subgrade flatness detection method, which can be applied to a corresponding image processing terminal, such as Figure 1 As shown, the method comprises the following steps: S1. Obtain an initial gap detection image between the road subgrade surface and the straight ruler collected by the image collector along the length direction of the straight ruler, wherein the initial gap detection image contains the road subgrade surface and the straight ruler.

[0022] In specific implementation, a straight ruler with scale marks can be placed on the road subgrade in advance. If the road subgrade surface is uneven, a corresponding gap will be formed between the road subgrade surface and the straight ruler. At this time, the image collector can be used to collect the initial gap detection image between the road subgrade surface and the straight ruler by keeping a fixed distance from the straight ruler and sticking to the ground along the length direction of the straight ruler. The initial gap detection image contains the road subgrade surface and the straight ruler. The image collector uploads the collected initial gap detection image to the image processing terminal, and the subsequent image analysis is performed by the image processing terminal.

[0023] S2. Perform image rotation, scaling, and cropping processing on the initial gap detection image to obtain a gap detection image with fixed direction and fixed size, and cut the gap detection image into a plurality of gap detection sub-images based on the scale marks of the straight ruler on the gap detection image.

[0024] In specific implementation, after obtaining the initial gap detection image, the image processing terminal can perform preprocessing on the initial gap detection image, including image rotation, image scaling, and image cropping processing, to obtain a gap detection image with fixed direction and fixed size (i.e., fixed resolution). Then, the scale marks on the straight ruler in the gap detection image are determined by target detection, and the gap detection image is cut into a plurality of gap detection sub-images based on the scale marks of the straight ruler as the reference boundary.

[0025] S3. Perform grayscale processing on each gap detection sub-image to obtain a corresponding gap detection sub-smooth grayscale image, and perform image binarization processing on each gap detection sub-smooth grayscale image to obtain a corresponding gap detection sub-binary image.

[0026] In implementation, the image processing terminal can substitute the three color channel component values R, G and B of each pixel point of the gap detection segmented image into the gray scale conversion formula H = 0.39 * R + 0.50 * G + 0.11 * B to obtain the gray scale value H of each pixel point, and construct a gap detection segmented gray scale image based on the gray scale value H of each pixel point; then perform median filter processing on the gap detection segmented gray scale image to obtain a gap detection segmented smooth gray scale image; and then perform image binarization processing on the gap detection segmented smooth gray scale image by using the maximum inter-class variance method to obtain a gap detection segmented binary image.

[0027] S4. Perform edge detection on the gap detection segmented binary image, determine each edge closed gap region in the gap detection segmented binary image, and extract a corresponding anchor gap region from each edge closed gap region.

[0028] In implementation, the image processing terminal can perform edge detection on the gap detection segmented binary image by using a Canny edge detection operator to determine each edge closed gap region in the gap detection segmented binary image; then determine the number of pixel points contained in each pixel column in the edge closed gap region, and take the number of pixel points as an anchor value of the corresponding pixel column; and finally take a region composed of pixel columns in which a plurality of consecutive anchor values are greater than a set reference threshold value as an anchor gap region.

[0029] S5. Take a direction perpendicular to the flat scale in the gap detection segmented binary image as a vertical direction, and take a direction parallel to the flat scale as a horizontal direction, and determine a vertical gap parameter and a horizontal gap parameter of each anchor gap region in the gap detection segmented binary image.

[0030] In implementation, the image processing terminal takes a direction perpendicular to the flat scale in the gap detection segmented binary image as a vertical direction (i.e., a direction perpendicular to a top boundary line of the anchor gap region as the vertical direction), and takes a direction parallel to the flat scale as a horizontal direction (i.e., a direction parallel to the top boundary line of the anchor gap region as the horizontal direction). Then determine the maximum image interval of the anchor gap region in the vertical direction, and take the maximum image interval of the anchor gap region in the vertical direction as the vertical gap parameter of the anchor gap region; determine the maximum image interval of the anchor gap region in the horizontal direction, and take the maximum image interval of the anchor gap region in the horizontal direction as the horizontal gap parameter of the anchor gap region.

[0031] S6. Based on the number of anchor gap regions contained and the vertical gap parameter and the horizontal gap parameter of each anchor gap region, construct a three-dimensional gap feature corresponding to the gap detection segmented binary image.

[0032] In implementation, the image processing terminal determines the number L of the anchor gap regions contained in the gap detection segmented binary image, determines the maximum value M of the vertical gap parameters of the anchor gap regions in the gap detection segmented binary image, and determines the maximum value N of the horizontal gap parameters of the anchor gap regions in the gap detection segmented binary image. Then, the three-dimensional gap features (L, M, N) of the gap detection segmented binary image are constructed by using L, M and N.

[0033] S7. The three-dimensional gap features of each gap detection segmented binary image are used to form a three-dimensional gap feature set, and the three-dimensional gap feature set is input into a preset flatness detection model for flatness detection to obtain the flatness detection result of the corresponding road subgrade section under the straight ruler, and the flatness detection result is output.

[0034] In implementation, the image processing terminal uses the three-dimensional gap features of each gap detection segmented binary image to form a three-dimensional gap feature set { (L1, M1, N1), (L2, M2, N2),..., (L n , M n , N n )}, and then inputs the three-dimensional gap feature set into a preset flatness detection model for flatness detection to obtain the flatness detection result of the corresponding road subgrade section under the straight ruler. Before flatness detection is performed by using the flatness detection model, the image processing terminal pre-constructs an initialized BP neural network model, and trains and optimizes the initialized BP neural network model by using a training set until a set training condition (such as a set number of training times, a recall rate, etc.) is met, to obtain a trained flatness detection model. The training set contains a plurality of three-dimensional gap feature set samples, and each three-dimensional gap feature set sample is labeled with a corresponding flatness label. The flatness detection result obtained by the flatness detection model after flatness detection is a corresponding flatness value, and the image processing terminal outputs and displays the flatness detection result, so that a detection personnel can directly view the flatness value of the road subgrade surface under the straight ruler.

[0035] The method of the embodiment can efficiently detect the gap distribution features between the road subgrade surface and the straight ruler by using machine vision detection technology, and then accurately determine the flatness of the road subgrade by using the gap distribution features. The method can replace the traditional manual length detection method, and improve the efficiency and reliability of road subgrade flatness detection.

[0036] Embodiment 2: The embodiment provides a road subgrade flatness detection system, as shown in Figure 2 FIG. 1, which includes an image acquisition unit, an image segmentation unit, an image conversion unit, a gap detection unit, a parameter determination unit, a feature construction unit, and a flatness detection unit, wherein: An image acquisition unit is used to acquire an initial gap detection image between the roadbed surface and the straight ruler acquired by an image acquisition device along the length direction of the straight ruler, wherein the initial gap detection image includes the roadbed surface and the straight ruler; An image segmentation unit is used to perform image rotation, scaling, and cropping on the initial gap detection image to obtain a gap detection image with a fixed direction and fixed size, and to cut the gap detection image into a plurality of gap detection segmented images based on the scale marks of the straight ruler on the gap detection image; An image conversion unit is used to perform grayscale processing on each gap detection segmented image to obtain a corresponding gap detection segmented smooth grayscale image, and perform image binarization processing on each gap detection segmented smooth grayscale image to obtain a corresponding gap detection segmented binary image; a gap detection unit, configured to perform edge detection on the gap detection segmented binary image, determine each edge closed gap region in the gap detection segmented binary image, and extract a corresponding anchor gap region from each edge closed gap region; a parameter determination unit, configured to determine vertical gap parameters and horizontal gap parameters of each anchor gap region in the gap detection segmented binary image, taking a direction perpendicular to the straight ruler in the gap detection segmented binary image as a vertical direction and a direction parallel to the straight ruler as a horizontal direction; A feature construction unit, configured to construct a three-dimensional gap feature corresponding to the gap detection segmented binary image based on the number of anchor gap regions contained and the vertical gap parameters and horizontal gap parameters of each anchor gap region; The flatness detection unit is used to use the three-dimensional gap features of each gap detection segmented binary image to form a three-dimensional gap feature set, input the three-dimensional gap feature set into a preset flatness detection model to perform flatness detection, obtain the flatness detection result of the corresponding road subgrade section under the straight ruler, and output the flatness detection result.

[0037] Example 3: This embodiment provides a roadbed flatness detection device, such as Figure 3 As shown, at the hardware level, it includes: Data interface, used to establish data connection between the processor and the image collector; a memory for storing instructions; The processor is used to read the instructions stored in the memory and execute the road subgrade flatness detection method in Example 1 according to the instructions.

[0038] Optionally, the device further comprises an internal bus, and the processor, the memory and the data interface are connected to each other through the internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0039] The memory can include, but is not limited to, a RAM (Random Access Memory), a ROM (Read Only Memory), a Flash Memory, a FIFO (First Input First Output) memory, a FILO (First In Last Out) memory, etc. The processor can be a general-purpose processor, including a CPU (Central Processing Unit), a NP (Network Processor), etc.; and can also be a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0040] Embodiment 4: The embodiment provides a computer readable storage medium, and instructions are stored on the computer readable storage medium. When the instructions are run on a computer, the computer executes the road subgrade flatness detection method in the embodiment 1. The computer readable storage medium is a carrier for storing data, and can include, but is not limited to, a floppy disk, a compact disc, a hard disk, a flash memory, a USB flash disk and / or a Memory Stick, etc. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0041] The embodiment also provides a computer program which, when running on a computer, performs the road roadbed flatness detection method in the embodiment 1. The computer can be a general computer, a special computer, a computer network or other programmable devices.

[0042] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for limiting the scope of protection of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for detecting the flatness of a road subgrade, characterized by, The method comprises the following steps: obtaining an initial gap detection image of a road subgrade surface and a straight ruler collected by an image collector along the length direction of the straight ruler, wherein the initial gap detection image comprises the road subgrade surface and the straight ruler; performing image rotation, scaling and cutting on the initial gap detection image to obtain a gap detection image with fixed direction and size, and cutting the gap detection image into a plurality of gap detection sub-images based on the scale marks of the straight ruler on the gap detection image; performing grayscale processing on each gap detection sub-image to obtain a corresponding gap detection sub-smooth grayscale image, and performing image binarization processing on each gap detection sub-smooth grayscale image to obtain a corresponding gap detection sub-binary image; performing edge detection on the gap detection sub-binary image to determine each edge closed gap region in the gap detection sub-binary image, and extracting a corresponding anchor gap region from each edge closed gap region; determining a vertical direction and a horizontal direction of the gap detection sub-binary image, wherein the vertical direction is perpendicular to the straight ruler, and the horizontal direction is parallel to the straight ruler, and determining a vertical gap parameter and a horizontal gap parameter of each anchor gap region in the gap detection sub-binary image; constructing a three-dimensional gap feature of the corresponding gap detection sub-binary image based on the number of anchor gap regions and the vertical gap parameter and the horizontal gap parameter of each anchor gap region; inputting a three-dimensional gap feature set composed of the three-dimensional gap features of each gap detection sub-binary image into a preset flatness detection model to perform flatness detection, obtaining a flatness detection result of a corresponding road subgrade section under the straight ruler, and outputting the flatness detection result.

2. The method for detecting the flatness of a road subgrade according to claim 1, characterized in that, The grayscale processing on each gap detection sub-image to obtain a corresponding gap detection sub-smooth grayscale image, and the image binarization processing on each gap detection sub-smooth grayscale image to obtain a corresponding gap detection sub-binary image, comprises: calculating the grayscale value H of each pixel point by substituting the three color channel component values R, G and B of each pixel point of the gap detection sub-image into the grayscale conversion formula H=0.39×R+0.50×G+0.11×B, and constructing a gap detection sub-grayscale image based on the grayscale value H of each pixel point; performing median filtering processing on the gap detection sub-grayscale image to obtain a gap detection sub-smooth grayscale image; performing image binarization processing on the gap detection sub-smooth grayscale image by using the maximum inter-class variance method to obtain a gap detection sub-binary image.

3. The method of claim 1, wherein, The edge detection on the gap detection sub-binary image to determine each edge closed gap region in the gap detection sub-binary image comprises: performing edge detection on the gap detection sub-binary image by using a Canny edge detection operator to determine each edge closed gap region in the gap detection sub-binary image.

4. The method of claim 1, wherein, The extraction of a corresponding anchor gap region from each edge closed gap region comprises: determining the number of pixel points contained in each pixel column in the edge closed gap region, and taking the number of pixel points as an anchor value of the corresponding pixel column; The region composed of the pixel columns in which the continuous multiple anchor values in the edge-closed gap region are all greater than the set reference threshold is taken as an anchor gap region.

5. The method for detecting the flatness of a road subgrade according to claim 1, wherein, The method further comprises: determining the vertical gap parameter and the horizontal gap parameter of each anchor gap region in the gap detection segmented binary image, comprising: determining the maximum image interval of the anchor gap region in the vertical direction, and taking the maximum image interval of the anchor gap region in the vertical direction as the vertical gap parameter of the anchor gap region; 6. The method of claim 1, wherein, determining the maximum image interval of the anchor gap region in the horizontal direction, and taking the maximum image interval of the anchor gap region in the horizontal direction as the horizontal gap parameter of the anchor gap region. The method further comprises: constructing the three-dimensional gap feature of the gap detection segmented binary image based on the number of the anchor gap regions contained and the vertical gap parameter and the horizontal gap parameter of each anchor gap region, comprising:

7. The method of claim 1, wherein, determining the number L of the anchor gap regions contained in the gap detection segmented binary image, determining the maximum value M of the vertical gap parameter of each anchor gap region in the gap detection segmented binary image, and determining the maximum value N of the horizontal gap parameter of each anchor gap region in the gap detection segmented binary image; constructing the three-dimensional gap feature (L, M, N) of the gap detection segmented binary image by using L, M and N.

8. A road sub-grade flatness detection system characterized by, Before inputting the three-dimensional gap feature set into the preset flatness detection model for flatness detection, the method further comprises: constructing an initialized BP neural network model, and training and optimizing the initialized BP neural network model by using a training set until a set training condition is met, to obtain a trained flatness detection model, wherein the training set contains a plurality of three-dimensional gap feature set samples, and each three-dimensional gap feature set sample is labeled with a corresponding flatness label. The system comprises an image acquisition unit, an image segmentation unit, an image conversion unit, a gap detection unit, a parameter determination unit, a feature construction unit and a flatness detection unit, wherein: The image acquisition unit is configured to acquire an initial gap detection image between a roadbed surface and a flat ruler captured by an image acquisition device along a length direction of the flat ruler, and the initial gap detection image contains the roadbed surface and the flat ruler. The image segmentation unit is configured to perform image rotation, scaling and cropping on the initial gap detection image to obtain a gap detection image with a fixed direction and a fixed size, and cut the gap detection image into a plurality of gap detection segmented images based on scale marks of the flat ruler on the gap detection image. The image conversion unit is configured to perform grayscale processing on each gap detection segmented image to obtain a corresponding gap detection segmented smooth grayscale image, and perform image binarization processing on each gap detection segmented smooth grayscale image to obtain a corresponding gap detection segmented binary image. The gap detection unit is configured to perform edge detection on the gap detection segmented binary image, determine each edge-closed gap region in the gap detection segmented binary image, and extract a corresponding anchor gap region from each edge-closed gap region. The parameter determination unit is configured to determine vertical gap parameters and horizontal gap parameters of each anchor gap region in the gap detection segmented binary image by taking a direction perpendicular to the flat ruler as a vertical direction and a direction parallel to the flat ruler as a horizontal direction in the gap detection segmented binary image. The feature construction unit is configured to construct a three-dimensional gap feature corresponding to the gap detection segmented binary image based on the number of anchor gap regions and the vertical gap parameters and the horizontal gap parameters of each anchor gap region. The flatness detection unit is configured to input a three-dimensional gap feature set composed of the three-dimensional gap features of each gap detection segmented binary image into a preset flatness detection model to perform flatness detection, obtain a flatness detection result of a corresponding roadbed section under the flat ruler, and output the flatness detection result.

9. A road subgrade flatness detection device characterized by, The memory is configured to store instructions. The processor is configured to read the instructions stored in the memory and execute the roadbed flatness detection method according to any one of claims 1-7. The computer program is configured to execute the roadbed flatness detection method according to any one of claims 1-7 when the computer program runs on the computer.

10. A computer program, characterized in that, ​

Images