Road disease identification method, system and device and storage medium

Abstract

The embodiment of the invention discloses a road disease identification method, system and device and a storage medium, and the method comprises the steps: obtaining the image information of a to-be-detected road, and carrying out the preprocessing of the image information; extracting a disease area of the preprocessed image; identifying the disease characteristics of the disease area, and judging the type of the disease according to the disease characteristics, thereby realizing automatic identification of the road disease and the disease type, and being high in efficiency, low in cost, safe and reliable. According to the invention, the corresponding pixel-based disease area physical quantity processing template is matched according to the type of the disease, and the physical quantity of the disease area is calculated through the pixel-based disease area physical quantity processing template, so that the calculation of physical quantity data such as actual size and area of the disease area is realized; and the accuracy and the reliability of a road disease detection result are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of intelligent transportation technology, and in particular, to a road hazard identification method, system, device and storage medium. Background Art

[0002] As an important part of transportation infrastructure, the health of roads directly affects traffic safety, transportation efficiency and economic development. With the increase in the number of vehicles and the increase in the frequency of use, various types of defects such as cracks, potholes, and peeling will inevitably appear on the road surface. These defects not only affect driving comfort, but may also lead to traffic accidents. In the past, road disease detection mainly relied on manual inspections and visual inspections. Although this method is intuitive, the results depend largely on the experience and technical level of the inspectors, and it is easy to miss or misjudge. It requires a lot of manpower and material resources, especially in large-scale road networks. It is difficult to achieve comprehensive coverage, and it is impossible to systematically record and statistically analyze, which is not conducive to long-term tracking of the development trend of the disease. In addition, inspectors face greater safety hazards when working on the road. Therefore, there is an urgent need for a road disease detection solution that automatically obtains road diseases, and maintenance personnel can directly handle the diseases based on the results of road disease detection.

[0003] The above problems need to be solved urgently. Summary of the invention

[0004] In order to solve the related technical problems, the present invention provides a road disease identification method, system, device and storage medium to solve the problems mentioned in the above background technology part.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for identifying road damage, the method comprising:

[0007] Acquire image information of the road to be inspected, and preprocess the image information;

[0008] Extracting the damaged area of ​​the preprocessed image;

[0009] Identifying disease characteristics of the diseased area, and determining the type of the disease based on the disease characteristics;

[0010] Matching a corresponding pixel-based physical quantity processing template of the diseased area according to the type of the disease;

[0011] The physical quantity of the damaged area is calculated by using the pixel-based damaged area physical quantity processing template.

[0012] As an optional implementation manner, the obtaining of image information of the road to be inspected includes:

[0013] Setting physical parameters of a camera that captures image information of the road to be inspected;

[0014] The camera collects image information of the road to be inspected.

[0015] As an optional implementation manner, the physical parameters of the camera that captures the image information of the road to be inspected are set, including:

[0016] Set the height of the camera center point from the ground and the horizontal angle of the camera viewing angle.

[0017] As an optional implementation manner, the preprocessing the image information includes:

[0018] The image information is subjected to denoising, and the denoised image is subjected to geometric correction and edge-preserving filtering to obtain a pre-processed image.

[0019] As an optional implementation manner, the calculating the physical quantity of the damaged area by using the pixel-based damaged area physical quantity processing template includes:

[0020] Mapping the diseased area to the diseased area physical quantity processing template, and retrieving a preset ratio coefficient between image pixels and pixels on the diseased area physical quantity processing template;

[0021] The physical quantity of the diseased area is obtained by performing calculation according to the pixel quantity of the diseased area and the proportional coefficient; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

[0022] As an optional implementation manner, extracting the damaged area of ​​the preprocessed image includes:

[0023] Calculating the ROI area (region of interest) of the preprocessed image;

[0024] The ROI region is identified to obtain the diseased region.

[0025] In a second aspect, an embodiment of the present invention provides a road damage identification system, which adopts the road damage identification method provided by the embodiment of the first aspect, including:

[0026] A road image acquisition module is used to acquire image information of the road to be inspected and pre-process the image information;

[0027] A diseased area extraction module, used to extract the diseased area of ​​the preprocessed image;

[0028] A disease type determination module, used to identify the disease characteristics of the disease area and determine the type of the disease according to the disease characteristics;

[0029] A physical quantity template matching module, used for matching the corresponding pixel-based physical quantity processing template of the disease area according to the type of the disease;

[0030] The disease physical quantity calculation module is used to calculate the physical quantity of the diseased area through the pixel-based diseased area physical quantity processing template.

[0031] As an optional implementation, the disease physical quantity calculation module is specifically used for:

[0032] The diseased area is mapped to the diseased area physical quantity processing template, and the ratio coefficient between the preset image pixels and the pixels on the diseased area physical quantity processing template is retrieved; calculation is performed based on the pixel quantity of the diseased area and the ratio coefficient to obtain the physical quantity of the diseased area; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

[0033] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory connected to the processor, wherein program data is stored in the memory, and the processor retrieves the program data stored in the memory to execute the road damage identification method provided in the embodiment of the first aspect above.

[0034] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the road damage identification method in the embodiment of the first aspect mentioned above.

[0035] The technical solution proposed in the embodiment of the present invention obtains the image information of the road to be inspected, and pre-processes the image information; extracts the damaged area of ​​the pre-processed image; identifies the damage features of the damaged area, and determines the type of the damage according to the damage features; matches the corresponding pixel-based damage area physical quantity processing template according to the type of the damage; and calculates the physical quantity of the damage area through the pixel-based damage area physical quantity processing template. The present invention realizes the automatic identification of road damage and damage types. Compared with the traditional road damage detection method, it is not only efficient and low-cost, but also safe and reliable. The present invention matches the corresponding pixel-based damage area physical quantity processing template according to the type of the damage, calculates the physical quantity of the damage area through the pixel-based damage area physical quantity processing template, realizes the calculation of physical quantity data such as the actual size and area of ​​the damage area, and improves the accuracy and reliability of road damage detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate and understand the technical solutions in the embodiments of the present invention, the following briefly introduces the background technology of the present invention and the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the contents of the embodiments of the present invention and these drawings without paying any creative work.

[0037] Figure 1 A schematic diagram of a road damage identification method flow chart provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a road damage identification method flow chart provided by an embodiment of the present invention;

[0039] Figure 3 This is a structural block diagram of a road damage identification system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the technical problems solved by the present invention, the technical solutions adopted and the technical effects achieved clearer, the technical solutions of the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1

[0042] Please refer to Figure 1 As stated, Figure 1 A schematic diagram of a road damage identification method according to an embodiment of the present invention. As shown in the figure, the road damage identification method according to the present embodiment includes:

[0043] S101. Obtain image information of the road to be inspected, and preprocess the image information;

[0044] S102. Extracting the damaged area of ​​the preprocessed image;

[0045] S103. Identify the disease characteristics of the diseased area, and determine the type of the disease according to the disease characteristics;

[0046] S104. Matching a corresponding pixel-based physical quantity processing template of the diseased area according to the type of the disease;

[0047] S105. Calculate the physical quantity of the diseased area by processing the physical quantity template of the diseased area based on pixels.

[0048] Exemplarily, the obtaining of image information of the road to be inspected includes:

[0049] Setting physical parameters of a camera that captures image information of the road to be inspected;

[0050] The camera collects image information of the road to be inspected.

[0051] Exemplarily, the step of setting the physical parameters of a camera that captures image information of the road to be inspected includes:

[0052] Set the height of the camera center point from the ground and the horizontal angle of the camera viewing angle.

[0053] Exemplarily, the preprocessing the image information includes:

[0054] The image information is subjected to denoising, and the denoised image is subjected to geometric correction and edge-preserving filtering to obtain a pre-processed image.

[0055] Exemplarily, the calculating the physical quantity of the damaged area by using the pixel-based damaged area physical quantity processing template includes:

[0056] Mapping the diseased area to the diseased area physical quantity processing template, and retrieving a preset ratio coefficient between image pixels and pixels on the diseased area physical quantity processing template;

[0057] The physical quantity of the diseased area is obtained by performing calculation according to the pixel quantity of the diseased area and the proportional coefficient; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

[0058] Exemplarily, the extracting the damaged area of ​​the preprocessed image includes:

[0059] Calculating the ROI area (region of interest) of the preprocessed image;

[0060] The ROI region is identified to obtain the diseased region.

[0061] The road damage identification method 100 proposed in this embodiment obtains image information of the road to be inspected and preprocesses the image information; extracts the damage area of ​​the preprocessed image; identifies the damage characteristics of the damage area, and determines the type of the damage based on the damage characteristics, thereby realizing automatic recognition of road damage and damage types. Compared with the traditional road damage detection method, it is not only efficient and low-cost, but also safe and reliable. The road damage identification method 100 proposed in this embodiment matches the corresponding pixel-based damage area physical quantity processing template according to the type of the damage, calculates the physical quantity of the damage area through the pixel-based damage area physical quantity processing template, realizes the calculation of physical quantity data such as the actual size and area of ​​the damage area, and improves the accuracy and reliability of the road damage detection results.

[0062] Embodiment 2

[0063] like Figure 2 As stated, Figure 2 A schematic diagram of a road damage identification method flow chart provided in an embodiment of the present invention.

[0064] As shown in the figure, the road damage identification method 200 in this embodiment includes:

[0065] S201. Obtain image information of the road to be inspected, and preprocess the image information;

[0066] S202. Extracting the diseased area of ​​the preprocessed image;

[0067] S203. Identify the disease characteristics of the diseased area, and determine the type of the disease according to the disease characteristics;

[0068] S204. Matching a corresponding pixel-based physical quantity processing template of the diseased area according to the type of the disease;

[0069] S205. Calculate the physical quantity of the diseased area by processing the physical quantity template of the diseased area based on pixels.

[0070] Exemplarily, the road damage identification method further includes:

[0071] S206. Obtain the geographical location information of the camera when collecting the image information of the road to be inspected, and calculate the geographical location information of the damaged area based on the preprocessed image and the geographical location information.

[0072] Exemplarily, the obtaining of image information of the road to be inspected includes:

[0073] Setting physical parameters of a camera that captures image information of the road to be inspected;

[0074] The camera collects image information of the road to be inspected.

[0075] In this embodiment, the physical parameters of the camera that captures the image information of the road to be inspected are set specifically including:

[0076] Set the height of the camera center point from the ground and the horizontal angle of the camera viewing angle.

[0077] Specifically, in this embodiment, due to the different heights of the camera center point from the ground and the different angles of the camera viewing angle to the horizontal, the range of the camera capturing road image information and the deflection angle and size of the image are all different. These parameters directly affect the subsequent processing of the road image.

[0078] Exemplarily, the preprocessing the image information includes:

[0079] The image information is subjected to denoising, and the denoised image is subjected to geometric correction and edge-preserving filtering to obtain a pre-processed image.

[0080] Specifically, the denoising process of the image information includes: performing a convolution operation on the image using a Gaussian kernel to smooth the image and reduce the influence of random noise; removing noise while maintaining the edge so that the road disease boundary is not blurred; replacing the median in the window around each pixel to eliminate salt and pepper noise. In this embodiment, geometric correction of the denoised image includes: transforming according to the intrinsic parameter matrix of the camera, correcting the deformation caused by the shooting angle, making the image plane parallel to the actual road surface, correcting the lens distortion, eliminating the barrel or pincushion distortion caused by the wide-angle lens, and restoring the true shape of the road graphics.

[0081] Exemplarily, the calculating the physical quantity of the damaged area by using the pixel-based damaged area physical quantity processing template includes:

[0082] Mapping the diseased area to the diseased area physical quantity processing template, and retrieving a preset ratio coefficient between image pixels and pixels on the diseased area physical quantity processing template;

[0083] The physical quantity of the diseased area is obtained by performing calculation according to the pixel quantity of the diseased area and the proportional coefficient; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

[0084] Specifically, the corresponding pixel-based physical quantity processing template of the diseased area is matched according to the type of the disease. In this embodiment, the corresponding pixel-based physical quantity processing template of the diseased area is trained for different disease types and stored in a physical quantity processing template library.

[0085] Specifically, in this embodiment, the ratio coefficient between the preset image pixels and the pixels on the physical quantity processing template of the diseased area directly determines the accuracy of the calculation of the physical quantity of the diseased area. By processing the pictures corresponding to each disease type at each acquisition angle, matching the processed pictures to the physical quantity processing template of the diseased area, and calculating the actual transformation ratio coefficient between the pixels in the diseased area and the pixel grid on the physical quantity processing template of the diseased area, the average of several transformation ratio coefficients is obtained, and finally the ratio coefficient between the diseased pixels under the disease pictures at each angle and the pixel grid on the physical quantity processing template of the diseased area is obtained and stored in the data.

[0086] Exemplarily, the extracting the damaged area of ​​the preprocessed image includes:

[0087] Calculating the ROI area of ​​the preprocessed image;

[0088] The ROI region is identified to obtain the diseased region.

[0089] In this embodiment, the ROI area can be selected as a fixed area frame selection or an automatic identification frame selection. Specifically, when the user selects a fixed area frame selection, the road image captured by the camera is framed according to the fixed area frame selection data, and the framed area is determined as the ROI area. When the user selects automatic identification frame selection, the type of disease on the road image will be roughly judged, and according to the roughly judged disease type, the preset frame selection area under the disease type will be matched, and the ROI area of ​​the image will be framed using the frame selection area. After the ROI area of ​​the road image is identified, subsequent processing of the road image object is all processing of the ROI area.

[0090] Specifically, in this embodiment, the acquisition of the geographical location information of the camera when collecting the image information of the road to be inspected, and the calculation of the geographical location information of the diseased area according to the preprocessed image and the geographical location information specifically include:

[0091] Obtaining geographical location information of the camera when collecting image information of the road to be inspected, wherein the address location information may be but is not limited to GPS information; performing deviation value calibration on the geographical location information of the camera to calculate the geographical location coordinate information when collecting the road image;

[0092] The offset position parameters of the road damage area on the road image are calculated, and the geographical location coordinate information when the road image is collected is adjusted according to the offset position parameters, so as to finally obtain the geographical location coordinate information of the road damage area.

[0093] Specifically, in this embodiment, the physical quantity calculation scheme of the diseased area is not limited to this. It can also be based on GPD data and map data, by obtaining the vehicle speed or wheel speed of the inspection vehicle to calculate the actual distance corresponding to each frame of the image, and converting the pixels of the image into actual physical distances, and calculating the physical quantity data of the diseased area based on the physical distances of multiple frames of images.

[0094] The road disease identification method 200 proposed in this embodiment obtains image information of the road to be inspected and pre-processes the image information; extracts the disease area of ​​the pre-processed image; identifies the disease characteristics of the disease area, and determines the type of the disease based on the disease characteristics, thereby realizing automatic identification of road diseases and disease types. Compared with the traditional road disease detection method, it is not only efficient and low-cost, but also safe and reliable. The road disease identification method 200 proposed in this embodiment matches the corresponding pixel-based disease area physical quantity processing template according to the type of the disease, calculates the physical quantity of the disease area through the pixel-based disease area physical quantity processing template, realizes the calculation of physical quantity data such as the actual size and area of ​​the disease area, and improves the accuracy and reliability of the road disease detection results. The road disease identification method 200 proposed in this embodiment can accurately calculate the geographical location coordinate information of the road disease area, thereby improving the high-precision positioning of road diseases.

[0095] Embodiment 3

[0096] This embodiment provides a road damage identification system 300, which adopts the road damage identification method provided in the above-mentioned embodiment 1 or embodiment 2, including:

[0097] The road image acquisition module 301 is used to acquire image information of the road to be inspected and pre-process the image information;

[0098] A defect area extraction module 302 is used to extract the defect area of ​​the preprocessed image;

[0099] The disease type determination module 303 is used to identify the disease characteristics of the disease area and determine the type of the disease according to the disease characteristics;

[0100] A physical quantity template matching module 304 is used to match the corresponding pixel-based physical quantity processing template of the disease area according to the type of the disease;

[0101] The disease physical quantity calculation module 305 is used to calculate the physical quantity of the diseased area through the pixel-based diseased area physical quantity processing template.

[0102] Exemplarily, the obtaining of image information of the road to be inspected includes:

[0103] Setting physical parameters of a camera that captures image information of the road to be inspected;

[0104] The camera collects image information of the road to be inspected.

[0105] In this embodiment, the physical parameters of the camera that captures the image information of the road to be inspected are set specifically including:

[0106] Set the height of the camera center point from the ground and the horizontal angle of the camera viewing angle.

[0107] Specifically, in this embodiment, due to the different heights of the camera center point from the ground and the different angles of the camera viewing angle to the horizontal, the range of the camera capturing road image information and the deflection angle and size of the image are all different. These parameters directly affect the subsequent processing of the road image.

[0108] Exemplarily, the preprocessing the image information includes:

[0109] The image information is subjected to denoising, and the denoised image is subjected to geometric correction and edge-preserving filtering to obtain a pre-processed image.

[0110] Specifically, the denoising process of the image information includes: performing a convolution operation on the image using a Gaussian kernel to smooth the image and reduce the influence of random noise; removing noise while maintaining the edge so that the road disease boundary is not blurred; replacing the median in the window around each pixel to eliminate salt and pepper noise. In this embodiment, geometric correction of the denoised image includes: transforming according to the intrinsic parameter matrix of the camera, correcting the deformation caused by the shooting angle, making the image plane parallel to the actual road surface, correcting the lens distortion, eliminating the barrel or pincushion distortion caused by the wide-angle lens, and restoring the true shape of the road graphics.

[0111] Exemplarily, the disease physical quantity calculation module is specifically used for:

[0112] The diseased area is mapped to the diseased area physical quantity processing template, and the ratio coefficient between the preset image pixels and the pixels on the diseased area physical quantity processing template is retrieved; calculation is performed based on the pixel quantity of the diseased area and the ratio coefficient to obtain the physical quantity of the diseased area; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

[0113] Specifically, the corresponding pixel-based physical quantity processing template of the diseased area is matched according to the type of the disease. In this embodiment, the corresponding pixel-based physical quantity processing template of the diseased area is trained for different disease types and stored in a physical quantity processing template library.

[0114] Specifically, in this embodiment, the ratio coefficient between the preset image pixels and the pixels on the physical quantity processing template of the diseased area directly determines the accuracy of the calculation of the physical quantity of the diseased area. By processing the pictures corresponding to each disease type at each acquisition angle, matching the processed pictures to the physical quantity processing template of the diseased area, and calculating the actual transformation ratio coefficient between the pixels in the diseased area and the pixel grid on the physical quantity processing template of the diseased area, the average of several transformation ratio coefficients is obtained, and finally the ratio coefficient between the diseased pixels under the disease pictures at each angle and the pixel grid on the physical quantity processing template of the diseased area is obtained and stored in the data.

[0115] Exemplarily, the extracting the damaged area of ​​the preprocessed image includes:

[0116] Calculating the ROI area of ​​the preprocessed image;

[0117] The ROI region is identified to obtain the diseased region.

[0118] In this embodiment, the ROI area can be selected as a fixed area frame selection or an automatic identification frame selection. Specifically, when the user selects a fixed area frame selection, the road image captured by the camera is framed according to the fixed area frame selection data, and the framed area is determined as the ROI area. When the user selects automatic identification frame selection, the type of disease on the road image will be roughly judged, and according to the roughly judged disease type, the preset frame selection area under the disease type will be matched, and the ROI area of ​​the image will be framed using the frame selection area. After the ROI area of ​​the road image is identified, subsequent processing of the road image object is all processing of the ROI area.

[0119] Specifically, in this embodiment, the acquisition of the geographical location information of the camera when collecting the image information of the road to be inspected, and the calculation of the geographical location information of the diseased area according to the preprocessed image and the geographical location information specifically include:

[0120] Obtaining geographical location information of the camera when collecting image information of the road to be inspected, wherein the address location information may be but is not limited to GPS information; performing deviation value calibration on the geographical location information of the camera to calculate the geographical location coordinate information when collecting the road image;

[0121] The offset position parameters of the road damage area on the road image are calculated, and the geographical location coordinate information when the road image is collected is adjusted according to the offset position parameters, so as to finally obtain the geographical location coordinate information of the road damage area.

[0122] Specifically, in this embodiment, the physical quantity calculation scheme of the diseased area is not limited to this. It can also be based on GPD data and map data, by obtaining the vehicle speed or wheel speed of the inspection vehicle to calculate the actual distance corresponding to each frame of the image, and converting the pixels of the image into actual physical distances, and calculating the physical quantity data of the diseased area based on the physical distances of multiple frames of images.

[0123] The road disease identification system 300 proposed in this embodiment obtains image information of the road to be inspected and pre-processes the image information; extracts the disease area of ​​the pre-processed image; identifies the disease characteristics of the disease area, and determines the type of the disease based on the disease characteristics, thereby realizing automatic identification of road diseases and disease types. Compared with the traditional road disease detection method, it is not only efficient and low-cost, but also safe and reliable. The road disease identification system 300 proposed in this embodiment matches the corresponding pixel-based disease area physical quantity processing template according to the type of the disease, calculates the physical quantity of the disease area through the pixel-based disease area physical quantity processing template, realizes the calculation of physical quantity data such as the actual size and area of ​​the disease area, and improves the accuracy and reliability of the road disease detection results. The road disease identification system 300 proposed in this embodiment can accurately calculate the geographical location coordinate information of the road disease area, thereby improving the high-precision positioning of road diseases.

[0124] Embodiment 4

[0125] This embodiment provides an electronic device, which includes a processor and a memory connected to the processor, wherein program data is stored in the memory, and the processor calls the program data stored in the memory to execute any road damage identification method such as any one of the above-mentioned embodiments 1 or 2.

[0126] Embodiment 5

[0127] This embodiment provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement any road damage identification method as described in the first or second embodiment above.

[0128] It should be noted that the above-mentioned readable storage medium can be implemented by 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0129] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A road disease identification method, characterized in that: include: Acquire image information of the road to be inspected, and preprocess the image information; Extracting the damaged area of ​​the preprocessed image; Identifying disease characteristics of the diseased area, and determining the type of the disease based on the disease characteristics; Matching a corresponding pixel-based physical quantity processing template of the diseased area according to the type of the disease; The physical quantity of the damaged area is calculated by using the pixel-based damaged area physical quantity processing template.

2. The road damage identification method according to claim 1, characterized in that: The step of obtaining image information of the road to be inspected includes: Setting physical parameters of a camera that captures image information of the road to be inspected; The camera collects image information of the road to be inspected.

3. The road damage identification method according to claim 2, characterized in that: The step of setting the physical parameters of the camera that captures the image information of the road to be inspected includes: Set the height of the camera center point from the ground and the horizontal angle of the camera viewing angle.

4. The road damage identification method according to claim 1, characterized in that: The preprocessing of the image information comprises: The image information is subjected to denoising, and the denoised image is subjected to geometric correction and edge-preserving filtering to obtain a pre-processed image.

5. The road damage identification method according to claim 1, characterized in that: The calculating the physical quantity of the damaged area by using the pixel-based damaged area physical quantity processing template comprises: Mapping the diseased area to the diseased area physical quantity processing template, and retrieving a preset ratio coefficient between image pixels and pixels on the diseased area physical quantity processing template; The physical quantity of the diseased area is obtained by performing calculation according to the pixel quantity of the diseased area and the proportional coefficient; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

6. The road damage identification method according to any one of claims 1 to 5, characterized in that: The step of extracting the damaged area of ​​the preprocessed image comprises: Calculating the ROI area of ​​the preprocessed image; The ROI region is identified to obtain the diseased region.

7. A road disease identification system, characterized in that: The system adopts the road damage identification method described in claim 1, comprising: A road image acquisition module is used to acquire image information of the road to be inspected and pre-process the image information; A diseased area extraction module, used to extract the diseased area of ​​the preprocessed image; A disease type determination module, used to identify the disease characteristics of the disease area and determine the type of the disease according to the disease characteristics; A physical quantity template matching module, used for matching the corresponding pixel-based physical quantity processing template of the disease area according to the type of the disease; The disease physical quantity calculation module is used to calculate the physical quantity of the diseased area through the pixel-based diseased area physical quantity processing template.

8. The road damage identification system according to claim 7, characterized in that: The disease physical quantity calculation module is specifically used for: The diseased area is mapped to the diseased area physical quantity processing template, and the ratio coefficient between the preset image pixels and the pixels on the diseased area physical quantity processing template is retrieved; calculation is performed based on the pixel quantity of the diseased area and the ratio coefficient to obtain the physical quantity of the diseased area; wherein the physical quantity includes but is not limited to the size data and area data of the diseased area.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory connected to the processor, wherein the memory stores program data, and the processor retrieves the program data stored in the memory to execute the road damage identification method as described in claim 1.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the road damage identification method as claimed in claim 1 when executed by a processor.

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