Road damage automatic detection and early warning method in road patrol

Abstract

The invention relates to the technical field of computer vision, and discloses a road damage automatic detection and early warning method in road patrol, which comprises the following steps: step S01, carrying out image acquisition on a detected road by using camera equipment, step S02, preprocessing the road image in the step S01, and carrying out early warning on the road image in the step S02; s03, feature extraction is carried out on the road damage features calibrated in the step S02, S04, initial fracture evaluation is carried out on the damage identification features, extracted in the step S03, in the detected road image, and S05, instability fracture evaluation is carried out on the damage identification features, extracted in the step S03, in the detected road image. The method for automatically detecting and early warning the road damage in the road inspection has the advantages that real-time monitoring and data analysis of the road are realized, and the inspection efficiency is improved. The method for automatically detecting and early warning the road damage in the road inspection comprises the following steps of: S06, evaluating the damage degree of the road damage of the detected road, S07, generating early warning information according to an evaluation result in the S06, and S08, completing automatic detection and early warning of the road damage.

Description

Technical Field

[0001] The present invention relates to the technical field of computer vision, and more particularly to a method for automatic detection and early warning of road damage during road inspection. Background Art

[0002] As an infrastructure for transportation, the safety of roads is directly related to the safety of vehicle driving and the lives and property of passengers. Road damages such as cracks, potholes, ruts, etc. not only affect the driving comfort but may also cause traffic accidents. Therefore, regular inspection of road damages, timely detection and repair of damages are important means to ensure road safety.

[0003] Traditional manual road inspection methods have problems such as low efficiency, low accuracy, and strong subjectivity, which are difficult to meet the requirements of modern traffic for road maintenance efficiency. The emergence of automatic detection and early warning technology for road damage can greatly improve the detection efficiency, reduce labor costs, and at the same time improve the accuracy and real-time performance of detection, providing a more scientific and reasonable basis for road maintenance.

[0004] In the existing road inspection and maintenance work, manual detection of road damage has problems such as low efficiency and poor accuracy. With the development of computer vision technology, it has become possible to use image processing and pattern recognition technologies for automatic detection and early warning of road damage. However, most current methods are limited to the preliminary identification of damages, lacking accurate assessment of damage degree and early warning mechanisms, and unable to achieve comprehensive management of road damage. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for automatic detection and early warning of road damage during road inspection to solve the problems existing in the above background art.

[0006] The present invention provides the following technical solution: A method for automatic detection and early warning of road damage during road inspection, including the following steps:

[0007] Step S01: Use a camera device to collect images of the road to be inspected: Install a high-resolution camera on a road inspection vehicle to take real-time pictures of the road to be inspected, and obtain images of the road to be inspected.

[0008] Step S02: Preprocess the road images in Step S01: Preprocess the collected images of the road to be inspected, and convert the images of the road to be inspected into a data form.

[0009] Step S03: Extract features of the calibrated road damage features in Step S02: Through image processing algorithms, identify damages in the road images to be inspected, and extract damage identification features in the road images to be inspected.

[0010] Step S04: Conduct an initial fracture assessment on the damage recognition features in the detected road image in Step S03: Establish an initial fracture assessment mathematical model based on the damage recognition features, and calculate the initial fracture assessment coefficient for the damage recognition of the detected road;

[0011] Step S05: Conduct an instability fracture assessment on the damage recognition features in the detected road image in Step S03: Establish an instability fracture assessment mathematical model based on the damage recognition features, and calculate the instability fracture assessment coefficient for the damage recognition of the detected road;

[0012] Step S06: Conduct a damage degree assessment on the road damage of the detected road: Establish a damage degree assessment model based on the initial fracture assessment coefficient and the instability fracture assessment coefficient of the detected road, and conduct a damage degree assessment on the road damage of the detected road;

[0013] Step S07: Generate a warning message based on the evaluation result in Step S06: Generate warning messages of different levels based on the evaluation result of the damage degree assessment, and send the warning message to the client;

[0014] Step S08: Complete the automatic detection and warning of road damage: Store the detected road damage information, warning message, and maintenance record data in the cloud server, and establish a road damage database.

[0015] Preferably, in the said Step S02, the specific content of preprocessing the collected image of the detected road and converting the image of the detected road into a data form is: Convert the collected road image into a grayscale image, extract the image data in the grayscale image, and convert the image of the detected road into a data form.

[0016] Preferably, in the said Step S03, apply an edge detection algorithm to process the preprocessed image, conduct damage recognition on the detected road image, match the damage features of the preprocessed image according to a preset damage feature template, and extract the damage recognition features in the image through template matching;

[0017] The damage recognition features in the image include: the damage length recognized during the damage recognition of the detected road, the damage thickness recognized during the damage recognition of the detected road, the maximum pressure that the detected road can bear, the damage area recognized during the damage recognition of the detected road, the stress at the tip of the initial crack, the tensile strength of the detected road, the initial crack length, the crack length variable, and the crack depth of the initial crack recognized during the damage recognition of the detected road.

[0018] Preferably, in the said Step S04, the specific content of establishing an initial fracture assessment mathematical model based on the damage recognition features and calculating the initial fracture assessment coefficient for the damage recognition of the detected road is as follows:

[0019] Step S11: Calculate the elastic coefficient for detecting road damage identification. The calculation formula is as follows: where α represents the elastic coefficient for detecting road damage identification, h represents the length of the damage identified during the process of identifying road damage, H represents the thickness of the damage identified during the process of identifying road damage, E represents the elastic modulus of the road to be detected, and Pmax represents the maximum pressure that the road to be detected can bear;

[0020] Step S12: Calculate the crack initiation load coefficient for detecting road damage identification. The calculation formula is as follows: where Fl represents the crack initiation load coefficient for detecting road damage identification;

[0021] Step S13: Calculate the initial fracture evaluation coefficient for detecting road damage identification. The calculation formula is as follows: where Ka represents the initial fracture evaluation coefficient for detecting road damage identification, and S represents the damage area identified during the process of identifying road damage.

[0022] Preferably, in step S05, a mathematical model for instability fracture evaluation is established based on the damage identification characteristics, and the specific content of calculating the instability fracture evaluation coefficient for detecting road damage identification is as follows:

[0023] Step S11: Calculate the viscous stress of the initial crack identified during the process of identifying road damage. The calculation formula is as follows: where fn represents the viscous stress of the initial crack identified during the process of identifying road damage, f s represents the stress at the tip of the initial crack, d w represents the tensile strength of the road to be detected, a0 represents the length of the initial crack, Δa0 represents the crack length variable, and m represents the cohesion distribution index;

[0024] Step S12: Calculate the instability fracture evaluation coefficient for detecting road damage identification. The calculation formula is as follows: where Kb represents the instability fracture evaluation coefficient for detecting road damage identification, B represents other factors affecting the instability fracture of road damage, D represents the crack depth of the initial crack identified during the process of identifying road damage, and fn represents the viscous stress of the initial crack identified during the process of identifying road damage.

[0025] Preferably, in step S06, a damage degree evaluation model is established based on the initial fracture evaluation coefficient and the instability fracture evaluation coefficient of the road to be detected, and the specific content of evaluating the damage degree of the road damage of the road to be detected is as follows:

[0026] An evaluation model for the degree of damage is established based on the initial fracture evaluation coefficient and the instability fracture evaluation coefficient of the detected road, and the calculation formula is: Where Zb represents the road damage evaluation index of the detected road, and k1 and k1 represent constants;

[0027] The road damage evaluation index Zb of the detected road is compared with the preset road damage evaluation threshold θ. If the road damage evaluation index Zb of the detected road is less than the preset road damage evaluation threshold θ, it is determined that the degree of road damage is low. If the road damage evaluation index Zb of the detected road is greater than or equal to the preset road damage evaluation threshold θ, it is determined that the degree of road damage is high.

[0028] Preferably, in step S07, different levels of warning information are generated based on the evaluation results of the damage degree evaluation. When it is determined that the degree of road damage is low, a low-level warning information is sent to the client, and road maintenance personnel are arranged for maintenance; when it is determined that the degree of road damage is high, a high-level warning information is sent to the client, and the high-level warning damage area is displayed with a red light warning on the client, and road maintenance personnel are arranged for immediate maintenance.

[0029] Preferably, in step S08, the detected road damage information, warning information, and maintenance record data are stored in the cloud server, a road damage database is established, and the stored road damage database is analyzed regularly by the cloud server to identify the patterns and trends of road damage and optimize the road maintenance plan and resource allocation.

[0030] The technical effects and advantages of the present invention:

[0031] The present invention is provided with step S01: using a camera device to collect images of the detected road, step S02: preprocessing the road images in step S01, step S03: extracting features of the calibrated road damage features in step S02, step S04: performing an initial fracture evaluation on the damage recognition features in the detected road images extracted in step S03, step S05: performing an instability fracture evaluation on the damage recognition features in the detected road images extracted in step S03, step S06: evaluating the degree of road damage of the detected road, step S07: generating warning information based on the evaluation results in step S06, step S08: completing the automatic detection and warning of road damage. A method for automatic detection and warning of road damage in road inspections realizes real-time monitoring and data analysis of roads and improves the inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of a method for automatic detection and warning of road damage in road inspections. DETAILED DESCRIPTION OF THE INVENTION

[0033] The technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Additionally, the forms of the various structures described in the following embodiments are merely examples. A method for automatic detection and early warning of road damage during road inspection according to the present invention is not limited to the various structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0034] As Figure 1 shown, the present invention provides a method for automatic detection and early warning of road damage during road inspection, including the following steps:

[0035] Step S01: Use a camera device to collect images of the road to be detected: Install a high-resolution camera on the road inspection vehicle to take real-time pictures of the road to be detected and obtain images of the road to be detected.

[0036] Step S02: Preprocess the road images in Step S01: Preprocess the collected images of the road to be detected and convert the images of the road to be detected into a data form.

[0037] Step S03: Extract features of the calibrated road damage features in Step S02: Through an image processing algorithm, identify damage in the road image to be detected and extract damage identification features in the road image to be detected.

[0038] Step S04: Conduct an initial fracture assessment on the damage identification features in the road image to be detected extracted in Step S03: Establish an initial fracture assessment mathematical model based on the damage identification features and calculate the initial fracture assessment coefficient for damage identification of the road to be detected.

[0039] Step S05: Conduct an instability fracture assessment on the damage identification features in the road image to be detected extracted in Step S03: Establish an instability fracture assessment mathematical model based on the damage identification features and calculate the instability fracture assessment coefficient for damage identification of the road to be detected.

[0040] Step S06: Evaluate the degree of road damage to the road to be detected: Establish a damage degree assessment model based on the initial fracture assessment coefficient and the instability fracture assessment coefficient of the road to be detected, and evaluate the degree of road damage to the road to be detected.

[0041] Step S07: Generate early warning information based on the evaluation results in Step S06: Generate early warning information of different levels based on the evaluation results of the damage degree assessment and send the early warning information to the client.

[0042] Step S08: Complete the automatic detection and warning of road damage: Store the detected road damage information, warning information, and maintenance record data in the cloud server to establish a road damage database.

[0043] In this embodiment, it should be specifically noted that in step S01, a high-resolution camera is used to capture road surface damage, including road cracks and local potholes. The camera is equipped with a wide-angle lens to clearly capture the road surface and the conditions on both sides.

[0044] In this embodiment, it should be specifically noted that in step S02, the collected images of the detected road are preprocessed. The specific content of converting the images of the detected road into a data form is as follows: Convert the collected road images into grayscale images, extract the image data in the grayscale images, and convert the images of the detected road into a data form.

[0045] In this embodiment, it should be specifically noted that in step S03, an edge detection algorithm is applied to process the preprocessed images, perform damage recognition on the detected road images, and perform damage feature matching on the preprocessed images according to a preset damage feature template. Through template matching, the damage recognition features in the images are extracted;

[0046] The damage recognition features in the images include: the damage length recognized during the damage recognition of the detected road, the damage thickness recognized during the damage recognition of the detected road, the maximum pressure that the detected road can bear, the damage area recognized during the damage recognition of the detected road, the stress at the tip of the initial crack, the tensile strength of the detected road, the initial crack length, the crack length variable, and the crack depth of the initial crack recognized during the damage recognition of the detected road.

[0047] In this embodiment, it should be specifically noted that in step S04, an initial fracture assessment mathematical model is established based on the damage recognition features, and the specific content of calculating the initial fracture assessment coefficient for the damage recognition of the detected road is as follows:

[0048] Step S11: Calculate the elastic coefficient for the damage recognition of the detected road. The calculation formula is: Where α represents the elastic coefficient for the damage recognition of the detected road, h represents the damage length recognized during the damage recognition of the detected road, H represents the damage thickness recognized during the damage recognition of the detected road, E represents the elastic modulus of the detected road, and Pmax represents the maximum pressure that the detected road can bear;

[0049] Step S12: Calculate the crack initiation load coefficient for the damage recognition of the detected road. The calculation formula is: Where Fl represents the crack initiation load coefficient for the damage recognition of the detected road;

[0050] Step S13: Calculate the initial fracture evaluation coefficient for detecting road damage recognition, and the calculation formula is as follows: Where Ka represents the initial fracture evaluation coefficient for detecting road damage recognition, and S represents the damage area recognized during the process of detecting road damage recognition.

[0051] In this embodiment, it should be specifically noted that in step S05, a mathematical model for evaluating unstable fracture is established based on the damage recognition characteristics, and the specific content of calculating the unstable fracture evaluation coefficient for detecting road damage recognition is as follows:

[0052] Step S11: Calculate the viscous stress of the initial crack recognized during the process of detecting road damage recognition, and the calculation formula is as follows: Where fn represents the viscous stress of the initial crack recognized during the process of detecting road damage recognition, f s represents the stress at the tip of the initial crack, d w represents the tensile strength of the detection road, a0 represents the length of the initial crack, Δa0 represents the crack length variable, and m represents the cohesion distribution index;

[0053] Step S12: Calculate the unstable fracture evaluation coefficient for detecting road damage recognition, and the calculation formula is as follows: Where Kb represents the unstable fracture evaluation coefficient for detecting road damage recognition, B represents other factors affecting the unstable fracture of road damage, D represents the crack depth of the initial crack recognized during the process of detecting road damage recognition, and fn represents the viscous stress of the initial crack recognized during the process of detecting road damage recognition.

[0054] In this embodiment, it should be specifically noted that in step S06, a damage degree evaluation model is established based on the initial fracture evaluation coefficient and the unstable fracture evaluation coefficient of the detection road, and the specific content of evaluating the damage degree of the road damage of the detection road is as follows:

[0055] Establish a damage degree evaluation model based on the initial fracture evaluation coefficient and the unstable fracture evaluation coefficient of the detection road, and the calculation formula is as follows: Where Zb represents the road damage evaluation index of the detection road, and k1 and k1 represent constants;

[0056] Compare the road damage evaluation index Zb of the detection road with the preset road damage evaluation threshold θ. If the road damage evaluation index Zb of the detection road is less than the preset road damage evaluation threshold θ, it is determined that the road damage degree is low. If the road damage evaluation index Zb of the detection road is greater than or equal to the preset road damage evaluation threshold θ, it is determined that the road damage degree is high.

[0057] In this embodiment, it should be specifically noted that in step S07, different levels of warning information are generated based on the evaluation results of the damage degree. When it is determined that the road damage degree is low, a low-level warning information is sent to the client, and road maintenance personnel are arranged for maintenance; when it is determined that the road damage degree is high, a high-level warning information is sent to the client, and the high-level warning damage area is displayed with a red light warning on the client, and road maintenance personnel are arranged for immediate maintenance.

[0058] In this embodiment, it should be specifically noted that in step S08, the detected road damage information, warning information, and maintenance record data are stored in the cloud server, a road damage database is established, and regular data analysis is performed on the stored road damage database through the cloud server to identify the patterns and trends of road damage and optimize the road maintenance plan and resource allocation.

[0059] The main difference between this embodiment and the prior art is that this embodiment includes step S01: using a camera device to collect images of the detected road, step S02: preprocessing the road images in step S01, step S03: extracting the features of the road damage features calibrated in step S02, step S04: performing an initial fracture assessment on the damage recognition features in the detected road images extracted in step S03, step S05: performing an instability fracture assessment on the damage recognition features in the detected road images extracted in step S03, step S06: evaluating the damage degree of the road damage of the detected road, step S07: generating warning information based on the evaluation results in step S06, and step S08: completing the automatic detection and warning of road damage. A method for automatic detection and warning of road damage during road inspection uses image processing and deep learning technologies to accurately identify road damage, automatically extract feature information in the image, and perform classification and recognition, greatly improving the accuracy of detection, realizing real-time monitoring and data analysis of the road, and improving the inspection efficiency.

[0060] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0061] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for automatic detection and early warning of road damage during road inspection, characterized in that: It includes the following steps: Step S01: Use a camera device to collect images of the detection road: Install a high-resolution camera on the road inspection vehicle to take real-time pictures of the detection road and obtain images of the detection road; Step S02: Preprocess the road images in Step S01: Preprocess the collected images of the detection road and convert the images of the detection road into a data form; Step S03: Extract the damage features calibrated in Step S02: Through an image processing algorithm, identify the damage to the detection road image and extract the damage identification features in the detection road image; Step S04: Conduct an initial fracture assessment on the damage identification features in the detection road image extracted in Step S03: Establish an initial fracture assessment mathematical model based on the damage identification features and calculate the initial fracture assessment coefficient for the damage identification of the detection road; Step S05: Conduct an instability fracture assessment on the damage identification features in the detection road image extracted in Step S03: Establish an instability fracture assessment mathematical model based on the damage identification features and calculate the instability fracture assessment coefficient for the damage identification of the detection road; Step S06: Evaluate the damage degree of the road damage on the detection road: Establish a damage degree assessment model based on the initial fracture assessment coefficient and the instability fracture assessment coefficient of the detection road and evaluate the damage degree of the road damage on the detection road; Step S07: Generate a warning message based on the evaluation result in Step S06: Generate warning messages of different levels based on the evaluation result of the damage degree assessment and send the warning messages to the client; Step S08: Complete the automatic detection and warning of road damage: Store the detected road damage information, warning messages, and maintenance record data in the cloud server and establish a road damage database.

2. The method for automatic detection and early warning of road damage during road inspection according to claim 1, characterized in that: In Step S01, a high-resolution camera is used to capture the damage on the road surface, including road cracks and local potholes. The camera is equipped with a wide-angle lens to clearly capture the road surface and the situations on both sides.

3. The method for automatic detection and early warning of road damage during road inspection according to claim 1, characterized in that: In Step S02, the specific content of preprocessing the collected images of the detection road and converting the images of the detection road into a data form is: Convert the collected road images into grayscale images, extract the image data in the grayscale images, and convert the images of the detection road into a data form.

4. A method for automatic detection and early warning of road damage during road inspection according to claim 1, characterized in that: In Step S03, apply an edge detection algorithm to process the preprocessed image, identify the damage to the detection road image, match the damage features of the preprocessed image according to a preset damage feature template, and extract the damage identification features in the image through template matching; The damage identification features in the image include: the damage length identified during the damage identification of the detection road, the damage thickness identified during the damage identification of the detection road, the maximum pressure that the detection road can bear, the damage area identified during the damage identification of the detection road, the stress at the tip of the initial crack, the tensile strength of the detection road, the initial crack length, the crack length variable, and the crack depth of the initial crack identified during the damage identification of the detection road.

5. A method for automatic detection and warning of road damage during road inspection according to claim 1, characterized in that: In the step S04, an initial fracture evaluation mathematical model is established based on the damage identification features, and the specific content of calculating the initial fracture evaluation coefficient for detecting road damage is as follows: Step S11: Calculate the elastic coefficient for detecting road damage identification. The calculation formula is: where α represents the elastic coefficient for detecting road damage identification, h represents the length of the damage identified during the process of identifying road damage, H represents the thickness of the damage identified during the process of identifying road damage, E represents the elastic modulus of the road to be detected, and Pmax represents the maximum pressure that the road to be detected can bear; Step S12: Calculate the cracking load coefficient for detecting road damage identification, and the calculation formula is as follows: where Fl represents the cracking load coefficient for detecting road damage identification; Step S13: Calculate the initial fracture evaluation coefficient for detecting road damage identification. The calculation formula is as follows: where Ka represents the initial fracture evaluation coefficient for detecting road damage identification, and S represents the damage area identified during the process of identifying damage to the detected road.

6. The method for automatic detection and early warning of road damage during road inspection according to claim 1, wherein: In the step S05, an instability fracture evaluation mathematical model is established based on the damage identification features, and the specific content of calculating the instability fracture evaluation coefficient for detecting road damage is as follows: Step S11: Calculate the viscous stress of the initial crack identified during the damage identification of the detected road surface. The calculation formula is as follows: where fn represents the viscous stress of the initial crack identified during the damage identification of the detected road surface, f s represents the stress at the tip of the initial crack, d w represents the tensile strength of the detected road surface, a0 represents the initial crack length, Δa0 represents the crack length variable, and m represents the cohesion distribution index; Step S12: Calculate the instability fracture evaluation coefficient for detecting road damage recognition. The calculation formula is as follows: Where Kb represents the instability fracture evaluation coefficient for detecting road damage recognition, B represents other factors affecting the instability fracture of road damage, D represents the crack depth of the initial crack identified during the process of detecting road damage recognition, and fn represents the viscous stress of the initial crack identified during the process of detecting road damage recognition.

7. A method for automatic detection and warning of road damage during road inspection according to claim 1, characterized in that: In the step S06, a damage degree evaluation model is established based on the initial fracture evaluation coefficient and the instability fracture evaluation coefficient of the detected road, and the specific content of evaluating the road damage degree of the detected road is as follows: Based on the initial fracture evaluation coefficient and instability fracture evaluation coefficient of the detected road, a damage degree evaluation model is established, and the calculation formula is: Among them, Zb represents the road damage evaluation index of the detected road, and k1 and k1 represent constants; Compare the road damage evaluation index Zb of the detected road with the preset road damage evaluation threshold θ. If the road damage evaluation index Zb of the detected road is less than the preset road damage evaluation threshold θ, it is determined that the road damage degree is low. If the road damage evaluation index Zb of the detected road is greater than or equal to the preset road damage evaluation threshold θ, it is determined that the road damage degree is high.

8. A method for automatic detection and early warning of road damage during road inspection according to claim 1, characterized in that: In the step S07, different levels of warning information are generated based on the evaluation results of the damage degree evaluation. When it is determined that the road damage degree is low, a low-level warning information is sent to the client, and road maintenance personnel are arranged for maintenance; When it is determined that the road damage degree is high, a high-level warning information is sent to the client, and the high-level warning damage area is red-light warning displayed on the client, and road maintenance personnel are arranged for immediate maintenance.

9. The method for automatic detection and early warning of road damage during road inspection according to claim 1, characterized in that: In the step S08, the detected road damage information, warning information, and maintenance record data are stored in the cloud server, a road damage database is established, and the stored road damage database is regularly analyzed through the cloud server to identify the patterns and trends of road damage and optimize the road maintenance plan and resource allocation.

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