A road condition quality detection method and system for highway engineering supervision

Abstract

The present invention discloses a road condition quality detection method and system for highway engineering supervision, which relates to the technical field of road condition quality detection, and includes: Y1, dividing the road to be detected into multiple detection areas; Y2, applying vibration to the detection area through a vibration response detection module, and collecting the vibration amplitudes for multiple circles; at the same time, collecting the vibration arrival times detected by multiple detection channels distributed radially; Y3, the road condition quality detection system constructs a spatial data grid corresponding to the current detection area based on the physical spatial position, vibration amplitude and arrival time data when collecting the vibration amplitude; Y4, the visual detection module acquires the visible light image of the current detection area; the infrared detection module acquires the infrared thermal imaging image of this area. The multi-dimensional fusion detection ability of the present invention is strong. By combining vibration propagation analysis, color change recognition and thermal anomaly recognition, it can comprehensively evaluate the road quality from three dimensions of structure, appearance and thermal state.

Description

Technical Field

[0001] The present invention relates to the technical field of road condition quality detection, and specifically to a method and system for detecting the road condition quality of highway engineering supervision. Background Technique

[0002] In the construction and supervision stages of highway engineering, the detection of road quality is an important link to ensure structural safety and service life. Common detection methods include manual visual inspection, measurement with a portable compactor, laser flatness detection, and the use of image processing to identify road surface cracks and potholes, etc. Some high-grade projects also use infrared thermal imaging to detect temperature distribution to assist in identifying thermal joints or construction temperature differences during the paving process.

[0003] After retrieval, Chinese Patent (Publication No.: CN115266336A) discloses a method for tracking and controlling the quality of road engineering. This patent includes the following steps: Step 1: Survey the soil base layer of the construction road, classify the road conditions of the base layer, and at the same time mark the positions with the worst road conditions in each section of the same grade; Step 2: Scan the construction road to establish a three-dimensional model, and at the same time assign colors to the road conditions of each grade in the model.

[0004] In the prior art, most are mainly single-point detection or single-dimensional analysis, and it is difficult to comprehensively identify the structural integrity, material uniformity, and composite abnormal areas of the road. Especially when facing newly built or roads to be accepted, there is a lack of integrated and high-precision structural analysis means. Therefore, the present invention proposes a method and system for detecting the road condition quality of highway engineering supervision. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for detecting the road condition quality of highway engineering supervision to solve the problems mentioned in the above background technique.

[0006] The present invention can be realized through the following technical solutions: A road condition quality detection system for highway engineering supervision includes a processing module, a vibration response detection module, a visual detection module, and an infrared detection module;

[0007] The vibration response detection module includes a vibration unit and a plurality of vibration detection units. The plurality of vibration detection units are arranged at equal distances in multiple circles with the vibration unit as the core, that is, the distance between each circle is the same;

[0008] And with the vibration unit as the center point, the vibration response module is provided with a plurality of radially arranged detection channels, and each circle of vibration detection units has a group of vibration detection units located in the corresponding detection channels;

[0009] During operation, the vibration unit applies vibrations to corresponding parts of the road, and each vibration detection unit detects the vibration amplitude and vibration arrival time of the road and uploads them to the processing module;

[0010] After receiving the data of each vibration amplitude and vibration arrival time, the processing module constructs a corresponding spatial data grid based on the physical positions of the monitoring vibration units;

[0011] The visual detection module is used to obtain visible light images of corresponding parts of the road and upload them to the processing module;

[0012] The infrared detection module is used to obtain infrared thermal imaging images of corresponding parts of the road and upload them to the processing module;

[0013] The processing module divides the road into multiple detection areas, and respectively obtains the spatial data grid, visible light image and infrared thermal imaging image of each detection area through the vibration response detection module, visual detection module and infrared detection module, and calculates the spatial data score, visible light score and infrared thermal imaging score respectively;

[0014] The processing module is provided with a grading standard table, and the grading standard table is based on structural labels, appearance labels and thermal imaging labels with multiple levels;

[0015] In this embodiment, the grading standard table is provided with labels of good, slightly abnormal, abnormal, significantly abnormal and severely abnormal from low to high for different levels of scores;

[0016] After generating the spatial data score, visible light score and infrared thermal imaging score in the corresponding detection area, the processing module generates structural labels, appearance labels and thermal imaging labels for each detection area respectively by matching with the grading standard table, so as to identify the quality level of the area in three dimensions of structural integrity, appearance quality and thermal process state.

[0017] A further technical improvement of the present invention lies in: the calculation method of the spatial data score includes the following steps:

[0018] S1. Convert the spatial layout of all vibration detection units in the spatial data grid into a two-dimensional data structure indexed by the detection channel direction and the layer index organized, and each vibration detection unit corresponds to a vibration amplitude value and a vibration arrival time value two sets of original line-of-sight distances;

[0019] Furthermore, a vibration response network is obtained: ;

[0020] S2. The processing module calculates the circumferential vibration amplitude consistency of each layer;

[0021] For all vibration detection units within each circle, calculate the average vibration amplitude value within the circle and the standard deviation ;

[0022] Subsequently, based on the average vibration amplitude value and the standard deviation (amplitude fluctuation) calculate the coefficient of variation: ;

[0023] S3. Average the coefficients of variation of all circles to obtain the amplitude scoring factor ;

[0024] ; where M is the total number of circle layers;

[0025] S4. On each detection channel k, calculate the time difference of vibration arrival between the outermost and innermost vibration detection units ;

[0026] Subsequently, based on the time difference of vibration arrival , calculate the vibration propagation speed , ; where and are the distances from the innermost vibration detection unit to the vibration unit and from the outermost vibration detection unit to the vibration unit, respectively;

[0027] S5. By calculating the average speed of the vibration propagation speed and the standard deviation , calculate the propagation scoring factor , ;

[0028] S6. Based on the amplitude scoring factor and the propagation scoring factor , calculate the spatial data score ;

[0029] where .

[0030] A further technical improvement of the present invention lies in: the method for obtaining the visible light score includes:

[0031] A1. The processing module preprocesses the corresponding visible light image, including denoising and color space conversion;

[0032] A2. Calculate the color difference of the preprocessed visible light image, detect the areas in the image where the color change exceeds the preset difference threshold, and use them as color change areas;

[0033] A3. Calculate the proportion of the color change area in the visible light image to obtain the color change proportion score. ;

[0034] ;

[0035] A4. Use edge detection or a deep learning model to identify cracks in the visible light image, and at the same time use morphological operations and threshold segmentation to detect whether there is a sunken area in the visible light image;

[0036] Sunken areas usually appear as local shadows or sudden color changes in the image;

[0037] A5. When cracks or sunken areas are detected in, reduce the visible light score to a preset value Z.

[0038] .

[0039] A further technical improvement of the present invention lies in: the method for obtaining the infrared thermal imaging score, including:

[0040] B1. The processing module judges temperature abnormal points. When the temperature at a certain position in the infrared thermal imaging image differs from the average temperature of the road by more than a preset temperature abnormal threshold, then judge this position as a temperature abnormal point, that is ; where, is the temperature value at position in the infrared thermal imaging image; is the average temperature of the current road, which is the mean value of the whole image or a local sliding window; is the preset temperature abnormal threshold;

[0041] B2. Sum up the temperature deviations of all temperature abnormal points to obtain a fusion metric value :

[0042] ; where, R is the set composed of all pixels in the infrared thermal imaging image;

[0043] B3. Normalize the fusion metric value to the infrared thermal imaging score, and the formula used is:

[0044] ; where, is the preset maximum allowable total temperature difference, which can be obtained by experiments or historical experience.

[0045] A further technical improvement of the present invention lies in that: the processing module grids and aligns the visible light image and the infrared thermal imaging image of the same detection area, and each grid is a comparison unit and has a unique index coordinate;

[0046] For the visible light image, the processing module matches the color change area with each grid of the visible light image to obtain a set of color abnormal grids;

[0047] For the infrared thermal imaging image, the processing module matches the temperature abnormal points with each grid of the infrared thermal imaging image to obtain a set of thermal abnormal grids;

[0048] And the processing module compares the set of color abnormal grids with the set of thermal abnormal grids to obtain the intersection of the set of color abnormal grids and the set of thermal abnormal grids;

[0049] The processing module compares the number of intersections of the set of color abnormal grids and the set of thermal abnormal grids with a preset intersection threshold;

[0050] If the number of intersections is greater than the intersection threshold, the apparent label and the thermal imaging label of the corresponding detection area are upgraded.

[0051] A further technical improvement of the present invention lies in that: the processing module judges the index coordinates of the intersection of the set of color abnormal grids and the set of thermal abnormal grids to judge whether it is a closed area. If it is a closed area, all the grids inside it are counted as the number of intersections.

[0052] A further technical improvement of the present invention lies in that: the processing module binds coordinates to each detection area and each detection area includes a structure label, an apparent label and a thermal imaging label;

[0053] For each detection area its spatially adjacent areas are determined;

[0054] For the structure label, the apparent label or the thermal imaging label, if a detection area and one or more of its adjacent detection areas have the same or higher-level labels, it is determined that the abnormal problems corresponding to the structure label, the apparent label or the thermal imaging label are in a continuous or spreading state in the detection area;

[0055] The processing module upgrades the corresponding structure label, apparent label or thermal imaging label in the corresponding detection area.

[0056] On the other hand, the present invention also proposes a road condition quality detection method for highway engineering supervision. This road condition quality detection method uses the above road condition quality detection system and includes the following steps:

[0057] Y1. Divide the road to be detected into multiple detection areas, and use a road condition quality detection system with a vibration response detection module, a visual detection module, and an infrared detection module to detect each detection area respectively;

[0058] Y2. Apply vibration to the detection area through the vibration response detection module, and collect the vibration amplitudes for multiple cycles;

[0059] At the same time, with the vibration of the detection area as the center, the vibration response detection module collects the vibration arrival times detected by multiple radially distributed detection channels;

[0060] Y3. The road condition quality detection system constructs a spatial data grid corresponding to the current detection area based on the physical spatial position, vibration amplitude, and arrival time data when collecting the vibration amplitude;

[0061] Y4. The visual detection module acquires the visible light image of the current detection area;

[0062] The infrared detection module acquires the infrared thermal imaging image of this area;

[0063] Y5. The road condition quality detection system calculates the spatial data score based on the spatial data grid, calculates the visible light score based on the visible light image, and calculates the infrared thermal imaging score based on the infrared thermal imaging image;

[0064] Y6. The road condition quality detection system matches the spatial data score, visible light score, and infrared thermal imaging score with a preset grading standard table to generate the structure label, appearance label, and thermal imaging label of the current detection area.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention has strong multi-dimensional fusion detection ability. By combining vibration propagation analysis, color change recognition, and thermal anomaly recognition, it can comprehensively evaluate the road quality from three dimensions: structure, appearance, and thermal state;

[0067] By collecting the vibration amplitude and propagation time, visible light image, and infrared thermal image in each detection area, respectively constructing a structure response data grid, color change data, and temperature anomaly model, it realizes the fusion perception and score calculation of multi-dimensional road conditions;

[0068] And the built-in label grading mechanism of the system can generate structure labels, appearance labels, and thermal imaging labels based on the scoring results, and further identify the intersection relationship between color anomaly grids and thermal anomaly grids, judge whether a closed anomaly area is formed, and then expand the recognition accuracy of the anomaly influence range. At the same time, through the logical comparison between adjacent detection area labels, it can also judge whether there is a spatial spread trend in structural or construction problems, providing a reference for subsequent construction re-inspection and maintenance decision-making. Brief Description of the Drawings

[0069] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings.

[0070] Figure 1 It is a flowchart of the road condition quality detection method of the present invention;

[0071] Figure 2 It is a system block diagram of the road condition quality detection system of the present invention. Detailed Embodiments

[0072] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features and their effects of the present invention.

[0073] Embodiment 1

[0074] Please refer to Figure 1-2 As shown, the present invention provides a road condition quality detection system for highway engineering supervision, including a processing module, a vibration response detection module, a visual detection module and an infrared detection module;

[0075] The vibration response detection module includes a vibration unit and a plurality of vibration detection units. The plurality of vibration detection units are centered around the vibration unit and are arranged in multiple concentric circles at equal intervals on its outer side, that is, the distance between each circle is the same;

[0076] And with the vibration unit as the center point, the vibration response module is provided with a plurality of radially arranged detection channels, and each circle of vibration detection units has a group of vibration detection units located in the corresponding detection channels;

[0077] During operation, the vibration unit applies vibration to the corresponding part of the road, and each vibration detection unit detects the vibration amplitude and vibration arrival time of the road and uploads them to the processing module;

[0078] After receiving the data of each vibration amplitude and vibration arrival time, the processing module constructs a corresponding spatial data grid based on the physical positions of the monitored vibration units;

[0079] The visual detection module is used to obtain the visible light image of the corresponding part of the road and upload it to the processing module;

[0080] The infrared detection module is used to obtain the infrared thermal imaging image of the corresponding part of the road and upload it to the processing module;

[0081] The processing module divides the road into multiple detection areas, and respectively obtains the spatial data grid, visible light image, and infrared thermal imaging image of each detection area through the vibration response detection module, visual detection module, and infrared detection module, and respectively calculates the spatial data score, visible light score, and infrared thermal imaging score;

[0082] The calculation method of the spatial data score includes the following steps:

[0083] S1. Convert the spatial layout of all vibration detection units in the spatial data grid into a two-dimensional data structure indexed by the detection channel direction and the layer index organized, and each vibration detection unit corresponds to a vibration amplitude value and a vibration arrival time value for two groups of original line-of-sight distances;

[0084] Furthermore, a vibration response network is obtained: ;

[0085] S2. The processing module calculates the circumferential vibration amplitude consistency of each layer:

[0086] For all vibration detection units within each circle, that is, N vibration detection units in the circle with a radius of r, calculate the average vibration amplitude value and the standard deviation (amplitude fluctuation) ;

[0087] Among them, ;

[0088] ;

[0089] In the formula, N is the number of vibration detection units in the corresponding layer;

[0090] Subsequently, based on the average vibration amplitude value and the standard deviation (amplitude fluctuation) calculate the coefficient of variation: ; If ∈[0.1]: The closer it is to 0, the more uniform it is, and the larger it is, the more significant the energy difference in each direction is;

[0091] S3. Average the coefficients of variation of all layers to obtain the amplitude scoring factor ;

[0092] ; In the formula, M is the total number of layers;

[0093] ∈[0.1], the higher its value, the stronger the circumferential energy consistency, and thus the better the material density and construction uniformity;

[0094] S4. On each detection channel k, calculate the time difference of vibration arrival between the outermost and innermost vibration detection units , ; Wherein, is the vibration arrival time of the outermost circle (the Mth circle) in the detection channel k; is the vibration arrival time of the innermost circle (the 1st circle) in the detection channel k;

[0095] Subsequently, based on the time difference of vibration arrival , calculate the vibration propagation speed , ; Wherein, and are the distances from the innermost vibration detection unit to the vibration unit and the distance from the outermost vibration detection unit to the vibration unit respectively;

[0096] S5. Evaluate the consistency of the vibration propagation speeds of all detection channels k = 1...P , including:

[0097] Calculate the average speed , ;

[0098] Calculate the standard deviation , ;

[0099] Calculate the propagation scoring factor , ;

[0100] ∈[0.1], the more stable the propagation speed (the speeds in all directions are similar), the higher the value, indicating that the structure is more continuous and the construction is more uniform;

[0101] S6. Based on the amplitude scoring factor and the propagation scoring factor , calculate the spatial data score ;

[0102] Wherein, .

[0103] The method for obtaining the visible light score includes:

[0104] A1. The processing module preprocesses the corresponding visible light image, including:

[0105] Denosing: Use median filtering or Gaussian filtering to remove image noise to prevent affecting subsequent color analysis;

[0106] Color space conversion: Convert the RGB image to the HSV or Lab color space for better analysis of color differences;

[0107] A2. Calculate the color difference of the preprocessed visible light image, detect the regions in the image where the color change exceeds the preset difference threshold, and take them as the color change regions;

[0108] In this embodiment, the Euclidean distance is used to measure the color difference , where ; in the formula, , and are the hue, saturation, and lightness values of the pixel point p respectively; , and are the hue, saturation, and lightness values of the adjacent pixel point q respectively;

[0109] A3. Calculate the proportion of the color change region in the visible light image to obtain the color change proportion score ;

[0110] ; where ranges from [0, 1]. If the proportion is high, it indicates that there are color differences in most areas of the road in the visible light image, and there may be problems such as uneven construction, aging, and peeling. If the proportion is low, it means that the road color in the visible light image is consistent and the apparent state is good;

[0111] A4. Use edge detection or a deep learning model to identify cracks in the visible light image, and at the same time use morphological operations and threshold segmentation to detect whether there are sunken areas in the visible light image;

[0112] Sunken areas usually appear as local shadows or sudden color changes in the image;

[0113] A5. When cracks or sunken areas are detected in , reduce the visible light score to the preset value Z;

[0114] ;

[0115] The final visible light score is jointly determined by the color change score and cracks and sunken areas. If no cracks or sunken areas are detected in the corresponding road of , then use the color change proportion score as the final visible light score ; if cracks or sunken areas are detected in the road corresponding to the visible light image, then use the preset value Z as the final visible light score , so as to greatly reduce the visible light score when there are cracks or sunken areas in the road.

[0116] Method for obtaining infrared thermal imaging score, including:

[0117] B1. The processing module determines temperature abnormal points. When the temperature at a certain position in the infrared thermal imaging image has a difference from the average temperature of the road greater than the preset temperature abnormal threshold, then this position is determined as a temperature abnormal point, that is ; where is the temperature value at position in the infrared thermal imaging image; is the average temperature of the current road, which adopts the whole map or local sliding window average value; is the preset temperature abnormal threshold;

[0118] B2. Sum up the temperature deviations of all temperature abnormal points to obtain the fusion metric value :

[0119] ; where R is the set composed of all pixels in the infrared thermal imaging image;

[0120] B3. Normalize the fusion metric value into the infrared thermal imaging score, and the formula used is:

[0121] ; where is the preset maximum allowable total temperature difference, which can be obtained by experiments or historical experience.

[0122] The processing module grids and aligns the visible light image and the infrared thermal imaging image in the same detection area, and each grid is a comparison unit and has a unique index coordinate;

[0123] For the visible light image, the processing module matches the color change area with each grid of the visible light image to obtain the color abnormal grid set;

[0124] For the infrared thermal imaging image, the processing module matches the temperature abnormal points with each grid of the infrared thermal imaging image to obtain the thermal abnormal grid set;

[0125] And the processing module compares the color abnormal grid set with the thermal abnormal grid set to obtain the intersection of the color abnormal grid set and the thermal abnormal grid set;

[0126] The processing module compares the number of intersections of the color abnormal grid set and the thermal abnormal grid set with the preset intersection threshold;

[0127] If the number of intersections is greater than the intersection threshold, then upgrade the apparent label and the thermal imaging label of the corresponding detection area.

[0128] The processing module is provided with a grading standard table, which is based on structure tags, appearance tags, and thermal imaging tags with multiple levels.

[0129] In this embodiment, for the scoring of different levels, the grading standard table is set with tags of good, slightly abnormal, abnormal, significantly abnormal, and severely abnormal from low to high.

[0130] After the processing module generates a spatial data score, a visible light score, and an infrared thermal imaging score in the corresponding detection area, by matching with the grading standard table, structure tags, appearance tags, and thermal imaging tags are respectively generated for each detection area, which are used to identify the quality level of the area in three dimensions of structural integrity, appearance quality, and thermal process status.

[0131] Embodiment 2

[0132] A road condition quality detection system for highway engineering supervision includes a processing module, a vibration response detection module, a visual detection module, and an infrared detection module.

[0133] The vibration response detection module includes a vibration unit and multiple vibration detection units. The multiple vibration detection units are centered around the vibration unit and are arranged at equal intervals in multiple circles outside it, that is, the spacing between each circle is the same.

[0134] And with the vibration unit as the center point, the vibration response module is provided with multiple radially arranged detection channels, and each circle of vibration detection units has a group of vibration detection units located in the corresponding detection channels.

[0135] During operation, the vibration unit applies vibration to the corresponding part of the road, and each vibration detection unit detects the vibration amplitude and vibration arrival time of the road and uploads them to the processing module.

[0136] After the processing module receives the data of each vibration amplitude and vibration arrival time, based on the physical positions of the monitored vibration units, a corresponding spatial data grid is constructed.

[0137] The visual detection module is used to obtain the visible light image of the corresponding part of the road and upload it to the processing module.

[0138] The infrared detection module is used to obtain the infrared thermal imaging image of the corresponding part of the road and upload it to the processing module.

[0139] The processing module divides the road into multiple detection areas, and respectively obtains the spatial data grid, visible light image, and infrared thermal imaging image of each detection area through the vibration response detection module, the visual detection module, and the infrared detection module, and respectively calculates the spatial data score, visible light score, and infrared thermal imaging score.

[0140] Calculation method for spatial data scoring, comprising the following steps:

[0141] S1. Convert the spatial layout of all vibration detection units in the spatial data grid into a two-dimensional data structure indexed by the detection channel direction and the layer index organized, and each vibration detection unit corresponds to a vibration amplitude value and a vibration arrival time value for two sets of original line-of-sight distances;

[0142] Thereby obtaining a vibration response network;

[0143] S2. The processing module calculates the circumferential vibration amplitude consistency of each layer:

[0144] For all vibration detection units within each circle, calculate the average vibration amplitude value and the standard deviation ;

[0145] Subsequently, based on the average vibration amplitude value and the standard deviation (amplitude fluctuation) calculate the coefficient of variation: ;

[0146] S3. Average the coefficients of variation of all layers to obtain an amplitude scoring factor ;

[0147] ; where M is the total number of layers;

[0148] S4. On each detection channel k, calculate the vibration arrival time difference between the outermost and innermost vibration detection units ;

[0149] Subsequently, based on the vibration arrival time difference , calculate the vibration propagation speed , ; where and are the distances from the innermost vibration detection unit to the vibration unit and the distance from the outermost vibration detection unit to the vibration unit, respectively;

[0150] S5. By calculating the average speed of the vibration propagation speed and the standard deviation , calculate the propagation scoring factor , ;

[0151] S6. Based on the amplitude scoring factor and the propagation scoring factor , calculate the spatial data scoring ;

[0152] Among them, 。

[0153] A method for obtaining a visible light score includes:

[0154] A1. The processing module preprocesses the corresponding visible light image;

[0155] A2. Calculate the color difference of the preprocessed visible light image, detect the area where the color change in the image exceeds the preset difference threshold, and use it as the color change area;

[0156] A3. Calculate the proportion of the color change area in the visible light image to obtain the color change proportion score ;

[0157] ;

[0158] A4. Use edge detection or a deep learning model to identify cracks in the visible light image, and at the same time use morphological operations and threshold segmentation to detect whether there is a concave area in the visible light image;

[0159] A5. When cracks or concavities are detected in, reduce the visible light score to the preset value Z;

[0160] Finally, 。

[0161] A method for obtaining an infrared thermal imaging score includes:

[0162] B1. The processing module determines temperature anomaly points. When the temperature at a certain position in the infrared thermal imaging image differs from the average temperature of the road by more than the preset temperature anomaly threshold, then this position is determined as a temperature anomaly point;

[0163] B2. Sum up the temperature deviations of all temperature anomaly points to obtain a fusion metric value :

[0164] ; In the formula, R is the set composed of all pixels in the infrared thermal imaging image; is the temperature value at the position in the infrared thermal imaging image; is the average temperature of the current road; is the preset temperature anomaly threshold;

[0165] B3. Through the formula Convert the fusion metric value Normalize to infrared thermal imaging score ;

[0166] Wherein, is the preset maximum allowable total temperature difference.

[0167] The processing module grids and aligns the visible light image and the infrared thermal imaging image of the same detection area, and each grid is a comparison unit and has a unique index coordinate;

[0168] For the visible light image, the processing module matches the color change area with each grid of the visible light image to obtain a set of color abnormal grids;

[0169] For the infrared thermal imaging image, the processing module matches the temperature abnormal points with each grid of the infrared thermal imaging image to obtain a set of thermal abnormal grids;

[0170] And the processing module compares the set of color abnormal grids with the set of thermal abnormal grids to obtain the intersection of the set of color abnormal grids and the set of thermal abnormal grids;

[0171] The processing module compares the number of intersections of the set of color abnormal grids and the set of thermal abnormal grids with a preset intersection threshold;

[0172] If the number of intersections is greater than the intersection threshold, upgrade the apparent label and the thermal imaging label of the corresponding detection area.

[0173] The processing module judges the index coordinates of the intersection of the set of color abnormal grids and the set of thermal abnormal grids to determine whether it is a closed area. If it is a closed area, all the grids inside it are counted as the number of intersections;

[0174] In this embodiment, the processing module extracts the index coordinates in the set intersection, denoted as the intersection grid index set;

[0175] The processing module constructs a binary mask image based on the intersection grid index set, assigns a value of "1" to the coordinates of the intersection grids in the mask, and the rest are "0", for spatial structure analysis;

[0176] On this basis, the processing module uses a contour extraction or connected component recognition algorithm to analyze whether there is a closed grid contour structure in the intersection mask image, that is, whether there is a closed area boundary formed by continuous enclosure of intersection grids;

[0177] If a contour boundary that meets the closing condition is recognized, the processing module further judges whether the contour is head-to-tail connected and the boundary is continuous, and determines it as a closed area; if it is a closed area, all the grids inside the contour are regarded as potential abnormal influence areas and can be used as supplementary units of the intersection abnormal area to participate in subsequent statistics and quality assessment.

[0178] The processing module is provided with a grading standard table, which is based on structure tags, appearance tags, and thermal imaging tags with multiple levels.

[0179] The processing module matches the spatial data score, visible light score, and infrared thermal imaging score with the grading standard table to generate structure tags, appearance tags, and thermal imaging tags corresponding to the corresponding levels of the detection areas.

[0180] The processing module binds coordinates to each detection area , and each detection area includes structure tags, appearance tags, and thermal imaging tags;

[0181] For each detection area , determine its spatially adjacent areas. For example, for detection area , its adjacent areas include , , , ;

[0182] For structure tags, appearance tags, or thermal imaging tags, if a detection area and one or more of its adjacent detection areas have the same or higher-level tags, it is determined that the abnormal problems corresponding to the structure tags, appearance tags, or thermal imaging tags are in a continuous or spreading state in the detection area.

[0183] The processing module upgrades the corresponding structure tags, appearance tags, or thermal imaging tags in the corresponding detection areas.

[0184] The above are only the preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to obtain equivalent embodiments with equivalent changes, but as long as it does not depart from the technical content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for detecting the road condition quality of highway engineering supervision, characterized in that: Including the following steps: Y1. Divide the road to be detected into multiple detection areas, and use a road condition quality detection system with a vibration response detection module, a visual detection module, and an infrared detection module to detect each detection area respectively; Y2. Apply vibration to the detection area through the vibration response detection module, and collect the vibration amplitudes of multiple cycles; At the same time, with the vibration of the detection area as the center, the vibration response detection module collects the arrival times of the vibrations detected by multiple radially distributed detection channels; Y3. The road condition quality detection system constructs a spatial data grid corresponding to the current detection area based on the physical spatial position, vibration amplitude, and arrival time data when collecting the vibration amplitude; Y4. The visual detection module is used to obtain the visible light image of the corresponding part of the road and upload it to the processing module; The infrared detection module is used to obtain the infrared thermal imaging image of the corresponding part of the road and upload it to the processing module; Y5. The processing module of the road condition quality detection system divides the road into multiple detection areas, and respectively obtains the spatial data grid, visible light image, and infrared thermal imaging image of each detection area through the vibration response detection module, visual detection module, and infrared detection module, and calculates the spatial data score, visible light score, and infrared thermal imaging score respectively; Y6. There is a grading standard table in the processing module, and the grading standard table is based on structural labels, appearance labels, and thermal imaging labels with multiple levels; The processing module matches the spatial data score, visible light score, and infrared thermal imaging score with the grading standard table to generate the structural label, appearance label, and thermal imaging label of the corresponding level for the corresponding detection area.

2. A road condition quality detection system for highway engineering supervision, characterized in that, The road condition quality detection system adopts the road condition quality detection method described in claim 1, including a processing module, a vibration response detection module, a visual detection module, and an infrared detection module; The vibration response detection module includes a vibration unit and multiple vibration detection units. The multiple vibration detection units are arranged at equal distances in multiple circles outside the vibration unit with the vibration unit as the core; and with the vibration unit as the center point, the vibration response module is provided with multiple radially arranged detection channels, and each circle of vibration detection units has a group of vibration detection units located in the corresponding detection channels; During operation, the vibration unit applies vibration to the corresponding part of the road, and each vibration detection unit detects the vibration amplitude and vibration arrival time of the road and uploads them to the processing module; After receiving the vibration amplitude and vibration arrival time data, the processing module constructs a corresponding spatial data grid based on the physical positions of the monitoring vibration units; The visual detection module is used to obtain the visible light image of the corresponding part of the road and upload it to the processing module; The infrared detection module is used to obtain the infrared thermal imaging image of the corresponding part of the road and upload it to the processing module; The processing module divides the road into multiple detection areas, and respectively obtains the spatial data grid, visible light image, and infrared thermal imaging image of each detection area through the vibration response detection module, visual detection module, and infrared detection module, and calculates the spatial data score, visible light score, and infrared thermal imaging score respectively; The processing module is provided with a grading standard table, which is based on structure tags, appearance tags, and thermal imaging tags with multiple levels; The processing module matches the spatial data score, visible light score, and infrared thermal imaging score with the grading standard table to generate structure tags, appearance tags, and thermal imaging tags corresponding to the levels of the corresponding detection area.

3. A road condition quality inspection system for highway engineering supervision according to claim 2, characterized in that The calculation method of the spatial data score includes the following steps: S1. Convert the spatial layout of all vibration detection units in the spatial data grid into a two-dimensional data structure indexed by the detection channel direction and the layer index organized, and each vibration detection unit corresponds to vibration amplitude values and vibration arrival time values for two sets of original line-of-sight distances, thereby obtaining a vibration response network; S2. The processing module calculates the circumferential vibration amplitude consistency of each layer; For all vibration detection units within each circle, calculate the average vibration amplitude value within the circle and the standard deviation ; Subsequently, based on the average vibration amplitude value and the standard deviation calculate the coefficient of variation: ; S3. Average the coefficient of variation of all layers to obtain the amplitude scoring factor ; ; where M is the total number of turns; S4. On each detection channel k, first calculate the time difference between the vibration arrival times of the outermost and innermost vibration detection units , and then calculate the vibration propagation speed ; ; wherein, and are respectively the distance from the innermost vibration detection unit to the vibration unit and the distance from the outermost vibration detection unit to the vibration unit; S5. By calculating the average velocity of the vibration propagation velocity and the standard deviation , calculate the propagation scoring factor , ; S6. Calculate the spatial data score based on the amplitude scoring factor and the propagation scoring factor ; Among them, .

4. A road condition quality detection system for highway engineering supervision according to claim 3, characterized in that, The acquisition method of the visible light score includes: A1. The processing module preprocesses the corresponding visible light image; A2. Calculate the color difference of the preprocessed visible light image, detect the area where the color change in the image exceeds the preset difference threshold, and use it as the color change area; A3. Calculate the proportion of the color change area in the visible light image to obtain the color change proportion score ; ； A4. Use edge detection or a deep learning model to identify cracks in the visible light image, and at the same time use morphological operations and threshold segmentation to detect whether there is a concave area in the visible light image; A5. When a crack or depression is detected in the visible light image, reduce the visible light score to a preset value Z; Finally, .

5. The road condition quality detection system for highway engineering supervision according to claim 4, characterized in that, The acquisition method of the infrared thermal imaging score includes: B1. The processing module determines the temperature anomaly points. When the temperature at a certain position in the infrared thermal imaging image differs from the average temperature of the road by more than the preset temperature anomaly threshold, this position is determined as a temperature anomaly point. ; B2. Aggregate the temperature deviations of all temperature anomaly points to obtain a fusion metric value : ; where R is the set composed of all pixels in the infrared thermal imaging image; is the position in the infrared thermal imaging image of the temperature value; is the average temperature of the current road; B3. By the formula normalize the fusion metric value into the infrared thermography score ; In the formula, is the preset maximum allowable total temperature difference.

6. The road condition quality detection system for highway engineering supervision according to claim 5, characterized in that, The processing module grids and aligns the visible light image and the infrared thermal imaging image of the same detection area, and each grid is a comparison unit and has a unique index coordinate; For the visible light image, the processing module matches the color change area with each grid of the visible light image to obtain a set of color abnormal grids; For the infrared thermal imaging image, the processing module matches the temperature abnormal points with each grid of the infrared thermal imaging image to obtain a set of thermal abnormal grids; And the processing module compares the set of color abnormal grids with the set of thermal abnormal grids to obtain the intersection of the set of color abnormal grids and the set of thermal abnormal grids; The processing module compares the number of intersections of the set of color abnormal grids and the set of thermal abnormal grids with a preset intersection threshold; If the number of intersections is greater than the intersection threshold, upgrade the appearance tags and thermal imaging tags of the corresponding detection area.

7. The road condition quality detection system for highway engineering supervision according to claim 6, characterized in that, The processing module judges the index coordinates of the intersection of the set of color abnormal grids and the set of thermal abnormal grids to judge whether it is a closed area. If it is a closed area, all the grids inside it are counted as the number of intersections.

8. A road condition quality detection system for highway engineering supervision according to claim 2, characterized in that, The processing module binds coordinates to each detection area ; For each detection area , determine its spatially adjacent areas; For structure tags, appearance tags, or thermal imaging tags, if a certain detection area and one or more adjacent detection areas have the same or higher-level tags, it is determined that the abnormal problems corresponding to the structure tags, appearance tags, or thermal imaging tags are in a continuous or spreading state in the detection area; The processing module upgrades the corresponding structure tags, appearance tags, or thermal imaging tags in the corresponding detection area.

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