CN116051499A - Pavement crack detection method and system - Google Patents
Pavement crack detection method and system Download PDFInfo
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- CN116051499A CN116051499A CN202310026607.6A CN202310026607A CN116051499A CN 116051499 A CN116051499 A CN 116051499A CN 202310026607 A CN202310026607 A CN 202310026607A CN 116051499 A CN116051499 A CN 116051499A
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- 230000009286 beneficial effect Effects 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
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Abstract
The invention discloses a pavement crack detection method and system, comprising the following steps: acquiring a pavement depth image; respectively carrying out edge detection on the pavement depth images by adopting different filtering operators, and fusing edge detection results to obtain pavement crack edges; filtering the edges of the pavement cracks by adopting a connected domain threshold segmentation method to extract pavement cracks, and obtaining pavement crack information of length, width and area according to the pavement crack information. And two different filtering operators are adopted to respectively detect edges, and the edge detection results are subjected to feature fusion in a mode of threshold segmentation and weighted addition, so that the detection accuracy of pavement cracks is improved compared with a single edge detection mode and a traditional crack detection mode using gray level images or RGB images.
Description
Technical Field
The invention relates to the technical field of pavement detection, in particular to a pavement crack detection method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Most of the pavements such as airports, roads and the like are asphalt and cement concrete pavements, and have a certain service life, and when the service life exceeds a certain service life, a series of damage problems are caused. Cracks are a common road surface disease, and if the road surface disease is not detected and repaired in time, the road surface disease is easily damaged seriously due to severe weather and load, and even accidents are further caused. Therefore, it is necessary to check the road surface condition in time, and to further develop the road surface maintenance scheme.
The crack detection of the airport runway and the highway pavement is carried out manually, the efficiency is low, the labor is greatly consumed, and the development of the air transportation industry and the highway transportation industry is difficult to meet.
In the automatic detection method of the pavement cracks, when the cracks are detected by using a gray level image or an RGB image, the defects of uneven illumination caused by textures of pavement background and pavement imaging are overcome, the edge characteristics of the cracks are easily covered in the face of a complex background environment, and the accuracy of detection results is low.
The common single edge detection method is limited by the selection of the edge threshold value, so that the robustness is general, the deviation between the crack detection result and the actual situation is large, and the accuracy is low.
Disclosure of Invention
In order to solve the problems, the invention provides a pavement crack detection method and a pavement crack detection system, which adopt two different filtering operators to respectively detect edges, and adopt a mode of threshold segmentation and then weighted addition to perform feature fusion on edge detection results.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a pavement crack detection method, including:
acquiring a pavement depth image;
respectively carrying out edge detection on the pavement depth images by adopting different filtering operators, and fusing edge detection results to obtain pavement crack edges;
filtering the edges of the pavement cracks by adopting a connected domain threshold segmentation method to extract pavement cracks, and obtaining pavement crack information of length, width and area according to the pavement crack information.
As an alternative implementation manner, a Sobel filter operator and a Canny filter operator are adopted to respectively perform Sobel edge detection and Canny edge detection processing on the pavement depth image.
As an alternative implementation manner, fusing the Sobel edge detection result and the Canny edge detection result in a mode of threshold segmentation and weighted addition; and respectively carrying out threshold segmentation on the Sobel edge detection result in the X and Y gradient directions, and carrying out equal proportion weighted addition on the threshold segmentation and the Canny edge detection result.
As an alternative embodiment, the edges of the pavement cracks are subjected to a closed-loop operation pretreatment before filtering by adopting a connected domain threshold segmentation method.
Alternatively, the length of the pavement crack is as follows: and (3) converting the pavement crack skeleton into single pixel width, counting the number of pixel points in the pavement crack skeleton, and multiplying the counted number of pixel points by the length represented by the corresponding unit pixel to obtain the length of the pavement crack.
As an alternative embodiment, after the pavement crack is skeletonized, a morphological treatment mode is adopted to carry out deburring treatment on the pavement crack skeleton.
As an alternative embodiment, the area of the pavement crack is obtained by multiplying the number of pavement crack pixel points before pavement crack skeletonization by the area corresponding to the real pavement unit pixel.
Alternatively, the width of the pavement crack is obtained by dividing the area of the pavement crack by the length.
In a second aspect, the present invention provides a pavement crack detection system comprising:
an image acquisition module configured to acquire a road surface depth image;
the edge detection module is configured to respectively detect edges of the road surface depth images by adopting different filtering operators, and fuse edge detection results to obtain edges of the road surface cracks;
the crack detection module is configured to filter the edges of the pavement cracks by adopting a connected domain threshold segmentation method so as to extract pavement cracks and obtain pavement crack information of length, width and area.
In a third aspect, the invention provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a pavement crack detection method and a pavement crack detection system, which adopt two different filtering operators to respectively carry out edge detection, adopt a mode of threshold segmentation and then weighted addition to carry out feature fusion on edge detection results, realize accurate detection of pavement cracks, and compared with a single edge detection mode and a traditional crack detection mode using gray level images or RGB images, the detection result of the pavement crack detection method is more accurate and has less interference.
The invention provides a pavement crack detection method and a pavement crack detection system, which realize accurate pavement crack detection based on a depth image, mainly acquire the depth image through a vehicle-mounted line scanning laser camera, then perform edge detection to integrally extract pavement cracks, and perform connected domain threshold segmentation on the obtained pavement cracks, so that the length, width and area of the pavement cracks can be accurately measured finally.
According to the method, when the length of the pavement crack is calculated, the pavement crack skeleton is subjected to morphological deburring, so that the detection precision of the length of the pavement crack is effectively improved, and the detection precision of the average width of the pavement crack is further improved.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of a pavement crack detection method provided in embodiment 1 of the present invention;
fig. 2 is a road surface depth image provided in embodiment 1 of the present invention;
fig. 3 is a gray scale map corresponding to a road surface depth image provided in embodiment 1 of the present invention;
fig. 4 is a schematic diagram of a Sobel X-direction edge detection result provided in embodiment 1 of the present invention;
fig. 5 is a schematic diagram of a Sobel Y-direction edge detection result provided in embodiment 1 of the present invention;
FIG. 6 is a schematic diagram of the Canny edge detection result provided in embodiment 1 of the present invention;
fig. 7 is a schematic diagram of the result after the fusion of the edge detection result provided in embodiment 1 of the present invention;
fig. 8 is a schematic diagram of the result after the connected domain threshold segmentation provided in embodiment 1 of the present invention;
FIG. 9 is a schematic diagram of the result of skeletonizing a pavement crack according to example 1 of the present invention;
fig. 10 is a schematic diagram of the result of deburring the pavement crack skeleton provided in example 1 of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
The embodiment provides a pavement crack detection method based on a depth image, as shown in fig. 1, specifically including the following steps:
acquiring a pavement depth image;
respectively carrying out edge detection on the pavement depth images by adopting different filtering operators, and fusing edge detection results to obtain pavement crack edges;
filtering the edges of the pavement cracks by adopting a connected domain threshold segmentation method to extract pavement cracks, and obtaining pavement crack information of length, width and area according to the pavement crack information.
In the embodiment, a vehicle-mounted line scanning laser camera is adopted to collect road surface depth images; the vehicle-mounted platform is simultaneously provided with an encoder, a high-precision GPS and a line scanning laser camera, along with the running of a vehicle, the encoder inputs pulse signals to the line scanning laser camera, the sampling frequency of the line scanning laser camera is different according to the different speeds of the vehicle, the GPS position information is recorded while the road surface depth image is collected, and the collected road surface depth image and the recorded GPS position information are transmitted into a computer for subsequent processing.
What needs to be specifically stated is: the road surface image acquired by the embodiment is a depth image, and can be understood as a distance image, which is not a generalized color and brightness image; the adopted camera is a line scanning laser vision camera, which can be understood as a camera projecting a laser line to the ground, then detecting the distortion degree of the laser to calculate the distance between the ground and the camera, thereby obtaining a depth image, and the line scanning laser vision camera can obtain the depth image and the gray level image at the same time.
Because the obtained overall data difference value of the pavement depth image is smaller, and crack detection cannot be directly carried out, in the embodiment, sobel edge detection and Canny edge detection processing are respectively carried out on the pavement depth image by adopting a Sobel filter operator and a Canny filter operator;
the Sobel filter operator and the Canny filter operator are used for extracting gradients of the depth image and removing noise in the image. To a certain extent, the Canny filter operator is an improvement of the Sobel filter operator, so that more accurate detection can be performed on the edge of an input image, and actual experiments show that the images respectively processed by the Sobel filter operator and the Canny filter operator have better effect on certain parts, so that the embodiment selects a mode of combining the two filter operators to perform crack detection, a pavement depth image before filtering is shown in fig. 2, a gray level image corresponding to the pavement depth image before filtering is shown in fig. 3, edge detection results in the Sobel X direction and the Y direction are shown in fig. 4-5, and a Canny edge detection result is shown in fig. 6.
In the embodiment, image feature fusion is performed on the Sobel edge detection result and the Canny edge detection result by adopting a mode of threshold segmentation and weighted addition; in the threshold segmentation part, since the crack noise data characteristics of the pavement depth image processed by the Sobel filter operator in the X gradient direction and the Y gradient direction are different, threshold segmentation is respectively carried out in the X gradient direction and the Y gradient direction, then equal proportion weighted addition is carried out on the pavement depth image processed by the Canny filter operator, and the final fused result is shown in fig. 7.
The edge detection results are fused to obtain the edges of the pavement cracks, and more isolated noise points exist in the results obtained at the moment, so that the filtering removal is carried out by adopting a connected domain threshold segmentation method in the embodiment; before the connected domain filtering, the edge image of the pavement crack is subjected to closed operation pretreatment, so that a certain part of the crack is prevented from being removed by mistake, and the finally extracted pavement crack is shown in fig. 8; as can be seen from comparing the initial gray level diagram shown in FIG. 3, the embodiment successfully extracts the pavement cracks and the ground pits with a certain size, and obtains better results.
In this embodiment, the process of detecting the length, width and area of the pavement crack includes:
(1) The length of the pavement crack; skeletonizing the pavement crack to the single pixel width; counting the number of pixel points in the pavement crack skeleton; and multiplying the counted number of the pixel points by the length represented by the corresponding unit pixel to obtain the length of the pavement crack.
In the process of detecting the length of the pavement crack, as the problem of more burrs are generated after the pavement crack is skeletonized, as shown in fig. 9, the pavement crack skeleton is subjected to deburring treatment in a morphological treatment mode, and the result of the pavement crack skeleton after deburring is shown in fig. 10, so that the error of extraction of the length of the pavement crack is reduced.
(2) Area of the pavement crack; the method is obtained by multiplying the number of pavement crack pixel points in the pavement crack detection result before skeletonizing by the area corresponding to the unit pixel of the corresponding real pavement.
(3) Width of the pavement crack; the average width is used instead, and the area of the pavement crack is divided by the length.
The pavement crack detection method based on the depth image can accurately detect pavement cracks of airport runways, roads and the like, can accurately measure the length, the width and the area of the cracks, and simultaneously records GPS position information in the process of crack extraction.
Example 2
The present embodiment provides a pavement crack detection system, including:
an image acquisition module configured to acquire a road surface depth image;
the edge detection module is configured to respectively detect edges of the road surface depth images by adopting different filtering operators, and fuse edge detection results to obtain edges of the road surface cracks;
the crack detection module is configured to filter the edges of the pavement cracks by adopting a connected domain threshold segmentation method so as to extract pavement cracks and obtain pavement crack information of length, width and area.
It should be noted that the above modules correspond to the steps described in embodiment 1, and the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method described in embodiment 1. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly embodied as a hardware processor executing or executed with a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. A pavement crack detection method, characterized by comprising:
acquiring a pavement depth image;
respectively carrying out edge detection on the pavement depth images by adopting different filtering operators, and fusing edge detection results to obtain pavement crack edges;
filtering the edges of the pavement cracks by adopting a connected domain threshold segmentation method to extract pavement cracks, and obtaining pavement crack information of length, width and area according to the pavement crack information.
2. A pavement crack detection method according to claim 1, wherein,
and respectively carrying out Sobel edge detection and Canny edge detection processing on the pavement depth image by adopting a Sobel filter operator and a Canny filter operator.
3. A pavement crack detection method as set forth in claim 2, characterized in that,
fusing the Sobel edge detection result and the Canny edge detection result by adopting a threshold segmentation and weighted addition mode; and respectively carrying out threshold segmentation on the Sobel edge detection result in the X and Y gradient directions, and carrying out equal proportion weighted addition on the threshold segmentation and the Canny edge detection result.
4. A pavement crack detection method according to claim 1, wherein,
and (3) before filtering by adopting a connected domain threshold segmentation method, carrying out closed operation pretreatment on the edges of the pavement cracks.
5. A pavement crack detection method according to claim 1, wherein,
the length of the pavement crack is as follows: and (3) converting the pavement crack skeleton into single pixel width, counting the number of pixel points in the pavement crack skeleton, and multiplying the counted number of pixel points by the length represented by the corresponding unit pixel to obtain the length of the pavement crack.
6. A pavement crack detection method as set forth in claim 5, wherein,
and after the pavement crack skeletons are formed, deburring treatment is carried out on the pavement crack skeletons in a morphological treatment mode.
7. A pavement crack detection method according to claim 1, wherein,
obtaining the area of the pavement crack by multiplying the number of pavement crack pixel points before pavement crack skeletonization by the area corresponding to the real pavement unit pixel;
the width of the pavement crack is obtained by dividing the area of the pavement crack by the length.
8. A pavement crack detection system, comprising:
an image acquisition module configured to acquire a road surface depth image;
the edge detection module is configured to respectively detect edges of the road surface depth images by adopting different filtering operators, and fuse edge detection results to obtain edges of the road surface cracks;
the crack detection module is configured to filter the edges of the pavement cracks by adopting a connected domain threshold segmentation method so as to extract pavement cracks and obtain pavement crack information of length, width and area.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of any of claims 1-7.
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| CN202310026607.6A CN116051499A (en) | 2023-01-09 | 2023-01-09 | Pavement crack detection method and system |
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