Road disease global detection system and method

Abstract

The invention relates to the technical field of road detection, and particularly discloses a road disease global detection system and method, the system comprises a mobile carrier, a positioning device, a shooting device, a laser scanning device, a Doppler distance measuring device, a ground penetrating radar detection device and a computer terminal; the positioning device, the adaptive device, the laser scanning device, the Doppler distance measuring device, the ground penetrating radar detection device and the computer terminal are all arranged on the mobile carrier. The method has the advantages that global disease detection of a road surface layer, a base layer and a soil base layer can be completed at the same time through one-time detection, and disease positions are recognized and delineated through a road disease recognition algorithm. According to the method, health condition global big data of target road detection is realized, and problematic same-point surface data and internal data are compared and associated, so that data support is provided for inferring a relationship between surface diseases and internal existence.

Description

Technical Field

[0001] The present invention relates to the technical field of road detection, and in particular to a road disease global detection system and method. Background Art

[0002] Pavement inspection and maintenance is a central link because the pavement is the structural layer that directly bears the driving load and natural factors, which is related to whether driving is safe, fast, economical and comfortable. At present, the detection and evaluation of pavement defects are mainly through manual visual inspection or other single rough detection methods, which have problems such as low detection efficiency, low accuracy and large limitations. At present, digital image detection methods have the problems of low success rate of automated detection and inability to detect other indicators at the same time. In the process of road construction quality inspection, the detection of indicators such as layer thickness is mostly carried out by drilling and sampling at random points according to the inspection regulations, and indoor analysis and processing. This type of detection method has a strong randomness and is not representative.

[0003] Defects of the prior art:

[0004] 1. For the detection of road surface defects, the traditional manual inspection method has the disadvantages of low detection efficiency, inconvenient recording, difficult tracking and management, and easy omissions. For the detection of internal road defects, the traditional detection method is mainly two-dimensional ground penetrating radar, which has the disadvantages of low efficiency, single imaging, poor real-time positioning ability, and inability to fully cover the detection. It can no longer meet the requirements of rapid, full-area, and high-precision inspection of defects in urban roads and highways that are currently developing at a high speed.

[0005] 2. Existing road surface disease detection mainly uses line laser scanning and image recognition. The existing line laser has low stability for high-speed vehicle movement and bumpy road sections, and its accuracy is affected to a certain extent. In addition, the acquisition design, transmission, and interpretation are complex and costly. Image recognition currently mainly relies on manual analysis to determine whether there are cracks, rutting, potholes, subsidence and other diseases. It has high requirements for analysis technicians, strong subjectivity, and low automation and intelligence. Existing conventional road underground internal disease detection mainly uses three-dimensional ground penetrating radar methods, whose antenna center frequencies are mainly medium and high frequencies, and the detection depth is limited. In the detection of internal hidden dangers in roads, it mainly solves road base problems and is mainly used for high-precision detection and investigation of shallow (within 3m) underground diseases. It cannot effectively detect and identify deep (below 3m) disease bodies or developing hidden dangers.

[0006] 3. The existing road disease detection indicators are relatively simple, either they are vehicle-mounted detection systems for surface diseases or vehicle-mounted detection systems for road underground diseases. They fail to integrate surface and internal disease detection into an integrated vehicle-mounted detection system to conduct full-area road disease detection, and are unable to compare and correlate surface disease data with internal detection data at the same problematic point, and are unable to predict the correlation between surface diseases and internal conditions.

[0007] In summary, there is an urgent need for a road disease global detection system and method to solve the problems in the existing technology. Summary of the invention

[0008] The present invention aims to provide a road disease global detection system and method, and the specific technical scheme is as follows:

[0009] A road disease global detection system, comprising a mobile carrier, a positioning device, a shooting device, a laser scanning device, a Doppler ranging device, a ground penetrating radar detection device and a computer terminal; the positioning device, the adapter device, the laser scanning device, the Doppler ranging device, the ground penetrating radar detection device and the computer terminal are all arranged on the mobile carrier;

[0010] The positioning device is used to provide the position coordinates of the mobile vehicle;

[0011] The photographing device is used to take photos of the road surface;

[0012] The laser scanning device is used to scan the road surface and obtain the road surface smoothness;

[0013] The Doppler distance measuring device is used to measure the length of the detected road surface;

[0014] The ground penetrating radar detection device is used to detect internal road diseases;

[0015] The computer terminal is provided with a road damage recognition algorithm, and the road damage recognition algorithm is used to identify and delineate road surface damage and road internal damage in road surface photos.

[0016] Optionally, the positioning device includes a GPS receiver, and the GPS receiver signal is connected to the computer terminal to achieve real-time positioning of the mobile vehicle.

[0017] Optionally, the GPS receiver includes a GPS antenna, and the GPS antenna is arranged at the center above the mobile vehicle.

[0018] Optionally, the photographing device includes a high-definition camera, which is arranged in front of the mobile vehicle and is used to take pictures of the road surface in front while the mobile vehicle is traveling.

[0019] Optionally, the laser scanning device includes a plurality of laser sensors, and the laser sensors are installed in front of the mobile vehicle in a linear arrangement. The plurality of laser sensors cooperate in front of the mobile vehicle to realize a laser point cloud detection area.

[0020] Optionally, the ground penetrating radar detection device includes a plurality of single-channel dual-frequency ground penetrating radar antennas, and the plurality of single-channel dual-frequency ground penetrating radar antennas constitute a dual-frequency multi-channel radar detector.

[0021] Optionally, the dual-frequency ground penetrating radar antenna includes two pairs of butterfly-shaped dipoles, corresponding to a low-frequency antenna and a high-frequency antenna respectively, the main frequency of the low-frequency antenna is 100-400 MHz, and the main frequency of the high-frequency antenna is 400-900 MHz.

[0022] Optionally, the computer terminal includes a ground penetrating radar terminal for detecting underground road defects, a multi-point laser and video device terminal for detecting surface road defects, and a satellite map terminal for real-time display and collection of road locations:

[0023] The ground penetrating radar detection terminal for underground road diseases is used to display and control the data of the ground penetrating radar detection device; the display includes: radar waveforms, two-dimensional profiles, and the location and type of road diseases collected by the ground penetrating radar detection device; the control includes: starting and stopping data collection and controlling the ground penetrating radar detection device;

[0024] The multi-point laser and video device detection terminal for road surface damage is used to execute a road damage identification algorithm, or to obtain a real-time linear graph or a real-time curve graph formed by a laser scanning device, or to control a video device to take a road surface photo and synchronize the road surface photo with the position coordinates;

[0025] The satellite map real-time display and collection road position terminal is used to control the positioning device to realize real-time observation and record the detailed position and current time of the detected road, and generate the position coordinates of the mobile vehicle.

[0026] Optionally, the road damage identification algorithm is specifically:

[0027] Collect image data of road surface diseases, road internal structures and road internal diseases, and construct surface disease image datasets and underground structure and disease image datasets after processing the image data;

[0028] A convolutional neural network model is constructed by using a convolutional neural network algorithm, and the convolutional neural network model is trained based on a surface disease image dataset and an underground structure and disease image dataset;

[0029] Test and verify the trained model to obtain the road damage recognition algorithm.

[0030] In addition, the present invention also includes a method for detecting road damage in the entire area, using the above-mentioned road damage detection system in the entire area, and the method steps are as follows:

[0031] Start the mobile vehicle and use the positioning device to obtain the position coordinates of the mobile vehicle in real time;

[0032] A laser scanning device is used to obtain laser scanning images, a photographing device is used to collect road surface photos, and a ground penetrating radar detection device is started to collect road radar images;

[0033] Use road damage recognition algorithms to identify and delineate road surface and internal road damage in laser scanning images, road photos, and road radar images;

[0034] Combine location coordinates to mark road surface defects and road internal defects to complete road defect detection

[0035] The application of the technical solution of the present invention has the following beneficial effects:

[0036] The present invention discloses a road disease global detection system and method. The system collects road information through a shooting device and a laser scanning device, and detects road surface and internal diseases through a road disease recognition algorithm. The system can complete the global disease detection of the road surface layer, base layer and soil base layer at one time, and analyzes and delineates the disease location through the road disease recognition algorithm. The present invention improves the data processing capability, analysis efficiency and discrimination accuracy for road disease detection.

[0037] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a structural schematic diagram of a road disease global detection system in a preferred embodiment of the present invention;

[0040] Figure 2 It is the laser scanning imaging in the preferred embodiment of the present invention;

[0041] Figure 3 is a road surface photograph in a preferred embodiment of the present invention;

[0042] Figure 4is a high frequency radar chart in a preferred embodiment of the present invention;

[0043] Figure 5 is a low-frequency radar chart in a preferred embodiment of the present invention;

[0044] Among them, 1-mobile carrier, 2-positioning device, 3-shooting device, 4-laser scanning device, 5-Doppler ranging device, 6-ground penetrating radar detection device. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] Example:

[0047] like Figure 1 As shown, this embodiment discloses a road disease global detection system, including a mobile vehicle 1, a positioning device 2, a shooting device 3, a laser scanning device 4, a Doppler ranging device 5, a ground penetrating radar detection device 6 and a computer terminal; the positioning device 2, the adapter device, the laser scanning device 4, the Doppler ranging device 5, the ground penetrating radar detection device 6 and the computer terminal are all arranged on the mobile vehicle 1;

[0048] The positioning device 2 is used to provide the position coordinates of the mobile vehicle 1; the positioning device 2 includes a GPS receiver, and the GPS receiver signal is connected to the computer terminal to realize real-time positioning of the mobile vehicle 1. The GPS receiver includes a GPS antenna, and the GPS antenna is arranged in the center above the mobile vehicle 1. In this embodiment, the system programs a set of algorithms through the distance between the GPS receiver and the laser scanning device 4 or the ground penetrating radar detection device 6, and automatically assigns the GPS coordinate signal received by the receiver to the position of the laser scanning device 4 or the ground penetrating radar detection device 6, which provides a guarantee for the later correlation and comparative analysis of surface diseases and internal diseases and the positioning of disease locations, facilitates the unified management of the database, and improves the accuracy of road disease identification.

[0049] The photographing device 3 is used to take photos of the road surface. In this embodiment, the photographing device 3 includes a high-definition camera, which is arranged in front of the mobile vehicle 1 and is used to take photos of the road surface in front during the driving of the mobile vehicle 1. In this embodiment, the high-definition camera is mainly used for real-time video detection, mainly to take pictures of whether there are obvious defects such as cracks and potholes on the road surface.

[0050] The laser scanning device 4 is used to scan the road surface and obtain the road surface flatness; the laser scanning device 4 includes a plurality of laser sensors, which are installed in front of the mobile vehicle 1 through a linear arrangement, and a plurality of laser sensors are coordinated in front of the mobile vehicle 1 to realize a laser point cloud detection area.

[0051] In this embodiment, the laser scanning device 4 is composed of point cloud laser sensors arranged linearly (5 cm each, a total of 41, with consistent arrangement height, and the number of laser sensors can also be encrypted as needed) to form a rectangular transmitting box with a length of about 2m, which is fixed in front of the comprehensive inspection vehicle. The height of each sensor from the ground is obtained according to the laser ranging principle, and multiple points form a straight line (normal), curve or broken line (disease). According to the linear shape, the flatness of the road, whether there are ruts, local settlement and potholes, etc. can be indirectly obtained. The advantage of the preferred multi-point laser (relative to the existing line laser) in this embodiment is that the high density of cloud points ensures accurate detection, adapts to high-speed vehicle movement, and the acquisition design, transmission, and interpretation are simple, low cost, and high stability. Even if one sensor is damaged, the normal operation of other sensors will not affect the detection quality.

[0052] The Doppler ranging device 5 is used to measure the length of the detected road surface; the Doppler ranging device 5 is installed on the radar frame behind the mobile vehicle 1. When the mobile vehicle 1 is started, on the one hand, the ground penetrating radar detection device 6 is triggered by the mobile vehicle 1 to collect data, and on the other hand, the radial relative motion speed of the target to the radar in the ground penetrating radar detection device 6 is measured according to the size of the Doppler frequency, and then the length of the detected road can be measured according to the time difference between the transmitted pulse and the received pulse.

[0053] The ground penetrating radar detection device 6 is used to detect internal road diseases; the ground penetrating radar detection device 6 includes a plurality of single-channel dual-frequency ground penetrating radar antennas, and a plurality of single-channel dual-frequency ground penetrating radar antennas form a dual-frequency multi-channel radar detector. The dual-frequency ground penetrating radar antenna includes two pairs of butterfly dipoles, corresponding to a low-frequency antenna and a high-frequency antenna, respectively. The main frequency of the low-frequency antenna is 100 to 400 MHz, and the main frequency of the high-frequency antenna is 400 to 900 MHz. In this embodiment, the ground penetrating radar detection device 6 uses shielding materials and timing control chips to ensure that high and low frequency parallel detection at the same point is not interfered with to the greatest extent, and uses real-time sampling high dynamic gain technology, so that the obtained radar data image is clear, the overall noise is small, the shallow resolution is very high, and the deep signal-to-noise ratio is high.

[0054] The ground penetrating radar detection device 6 in this embodiment can fundamentally solve the problem that the existing three-dimensional ground penetrating radar only detects the underground base layer (shallow part), and one detection can obtain three-dimensional stereoscopic data of the shallow part (within 3m) and deep part (below 3m) of the underground road respectively, and comprehensively display the size, position, shape and distribution of underground disease hidden dangers, and truly achieve a comprehensive investigation of the shallow and deep parts of the underground (internal) disease detection of the road.

[0055] It should be noted that the mobile vehicle 1 is a general small truck or commercial vehicle with a cab inside. The cab controls the vehicle speed and drives reasonably as required to detect all roads. In addition, the mobile vehicle 1 is also provided with an operation room, which is mainly used to control the start and stop of the dual-frequency three-dimensional ground penetrating radar, video and multi-point laser radar data collection, and to check whether the real-time data and the driving route are normal. In addition, the mobile vehicle 1 is also provided with a storage room, which is mainly used to store the dual-frequency three-dimensional ground penetrating radar. It is connected to the vehicle body through a bracket and can be detached to avoid the trouble caused by the integrated type in transportation.

[0056] The computer terminal is provided with a road damage recognition algorithm, and the road damage recognition algorithm is used to identify and delineate road surface damage and road internal damage in road surface photos.

[0057] In this embodiment, the computer terminal includes a ground penetrating radar terminal for detecting underground road defects, a multi-point laser and video device terminal for detecting surface road defects, and a satellite map terminal for real-time display and collection of road locations:

[0058] The ground penetrating radar detection terminal for underground road diseases is used to display and control the data of the ground penetrating radar detection device 6; the display includes: the radar waveform, two-dimensional profile and the location and type of road diseases collected by the ground penetrating radar detection device 6; the control includes: starting and stopping data collection and controlling the ground penetrating radar detection device 6;

[0059] The multi-point laser and video device detection terminal for road surface damage is used to execute a road damage identification algorithm, or to obtain a real-time linear graph or a real-time curve graph formed by the laser scanning device 4, or to control the video device to take a road surface photo and synchronize the road surface photo with the position coordinates;

[0060] The satellite map real-time display and collection road position terminal is used to control the positioning device 2 to achieve real-time observation and record the detailed position and current time of the detected road, and generate the position coordinates of the mobile vehicle 1.

[0061] Furthermore, the road damage identification algorithm is specifically:

[0062] Collect image data of road surface diseases, road internal structures and road internal diseases, and construct surface disease image datasets and underground structure and disease image datasets after processing the image data;

[0063] A convolutional neural network algorithm is adopted to construct a convolutional neural network model, and the convolutional neural network model is trained based on a surface disease image dataset and an underground structure and disease image dataset.

[0064] Test and verify the trained model to obtain the road damage recognition algorithm.

[0065] In this embodiment, a convolutional neural network (CNN) algorithm (a deep learning algorithm) is used. Before constructing the convolutional neural network model, a large amount of image data of road surface diseases (cracks, potholes and subsidence, etc.), road internal structures (pipelines, pipe trenches, culverts, etc.) and internal diseases (looseness, hollowing, voids and water-rich) need to be collected. After a series of image processing, a set of reasonable surface disease image data sets and a set of underground internal structure and disease image data sets are constructed; then the CNN model is established and trained, and finally the model results are tested and verified (the road surface disease image set is 1772, and the average recognition accuracy is more than 90%; the road internal structure and disease image set is 1131, and the average recognition accuracy is more than 85%. The algorithm in this embodiment can further optimize the accuracy when the number of samples is increased).

[0066] In addition, this embodiment also discloses a method for detecting road defects in the entire area. This embodiment conducts an entire road detection experiment through a short section (500m) of test road; then, data analysis is performed indoors to count the number and location of defects automatically identified; finally, the defect locations (GPS coordinates) identified by the detection are staked out on site to verify the accuracy of the automatic identification and delineation. The specific steps include:

[0067] Start mobile vehicle 1;

[0068] The laser scanning device 4 is used to obtain laser scanning images (such as Figure 2 As shown), a photographing device 3 is used to collect road surface photos (as shown Figure 3 As shown), start the ground penetrating radar detection device 6 to collect the road radar map (as shown Figure 4 The high frequency radar profile and Figure 5 The low-frequency radar profile shown); it should be noted that the laser scanning imaging is a real-time linear graph or curve graph formed by multi-point laser scanning, the shooting device 3 uses a high-definition camera to shoot the road surface to obtain high-definition road surface photos, and the ground-penetrating radar detection device 6 uses a low-frequency radar to collect internal road diseases within 10m underground, and uses a high-frequency radar to collect internal road diseases within 3m underground.

[0069] A road disease recognition algorithm is used to identify and delineate road surface diseases and road internal diseases in laser scanning images, road surface photos and road radar images; the road disease recognition algorithm in this embodiment is based on a convolutional neural network (CNN) algorithm with Batch-Normalization (BN layer). The addition of the BN layer can perform adaptive regularization calculations, wherein the regularization factor can be adaptively obtained by calculating the data objective function and the model objective function of each iteration. The requirements for the initial network (a large number of data sets) are not too high, the fitting effect is good, the network convergence speed can be faster, the network learning effect becomes better, and the instability of conventional deep learning network training can be improved.

[0070] Combined with the location coordinates (GPS coordinates), the road surface diseases and road internal diseases are marked to complete the road disease detection. In this embodiment, GPS data is used to coordinate the road surface and road internal road disease data, so that all detection data are synchronized to the same location, so that the road surface and internal diseases can be accurately located and intelligently identified in one test, forming a global big data of the health status of the target road detection, and comparing and correlating the surface data and internal data of the same point with problems, preliminarily inferring the relationship between surface diseases and internal existence, and providing favorable data for road management and maintenance.

[0071] In this embodiment, see Figure 2-5 , the surface diseases circled by the detection were compared with the internal diseases at the same point. During the detection process, local settlement and crack diseases (next to the well chamber) were automatically identified on the road surface. The high-frequency radar profile of the road interior (within the depth of 0.4-1.2m) at the same point showed that the soil was severely loose, and there were local cavities (within the depth of 0.8-1.2m), and pipelines were identified next to the diseases. The low-frequency radar profile showed that there were no obvious diseases in the deep part, and the main diseases were concentrated in the water-stable layer (base layer) inside the shallow road. Therefore, it was preliminarily judged that the surface cracks and settlement were caused by the buried underground internal pipelines and the loose backfill after the masonry around the well chamber.

[0072] This embodiment discloses a road disease global detection system and method, which has a simple structure and low design cost. For normal operation, it only needs to install the ground penetrating radar detection device 6 at the rear of the mobile vehicle 1, and all equipment lines can be connected for detection; the laser scanning device 4 and the shooting device 3 are fixedly set in front of the mobile vehicle 1, and detection can be carried out only by turning on the power; this embodiment only needs one vehicle travel to complete the collection of road surface, shallow (within 3m) and deep (below 3m) road disease data in a lane detection area at the same time, and the amount of data obtained is very rich, and the detected data is stable, which greatly improves the detection efficiency, detection accuracy and detection depth, reduces the omission rate and mismatch rate of repeated detection, and reduces the detection cost. The surface, shallow and deep disease data are correlated with each other at the same point, which helps to infer the relationship between surface diseases and internal existence, and infer the possible causes of road diseases. This embodiment solves the limitation of the existing method of detecting a single road indicator and the inability to associate surface defects with internal relationships at the same point. It also solves the problems of strong subjectivity, high omission rate and low efficiency in the current reliance on manual point-by-point analysis to delineate defects. It also solves the problem that the current three-dimensional vehicle-mounted ground penetrating radar mainly performs fine positioning of defects in the underground base (shallow part) of the road and cannot effectively detect defects in the deep road base.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A road disease global detection system, characterized in that: It includes a mobile carrier, a positioning device, a shooting device, a laser scanning device, a Doppler ranging device, a ground penetrating radar detection device and a computer terminal; the positioning device, the adapter device, the laser scanning device, the Doppler ranging device, the ground penetrating radar detection device and the computer terminal are all arranged on the mobile carrier; The positioning device is used to provide the position coordinates of the mobile vehicle; The photographing device is used to take photos of the road surface; The laser scanning device is used to scan the road surface and obtain the road surface smoothness; The Doppler distance measuring device is used to measure the length of the detected road surface; The ground penetrating radar detection device is used to detect internal road diseases; The computer terminal is provided with a road damage recognition algorithm, and the road damage recognition algorithm is used to identify and delineate road surface damage and road internal damage in road surface photos.

2. The road disease global detection system according to claim 1 is characterized in that: The positioning device includes a GPS receiver, and the GPS receiver signal is connected to the computer terminal to achieve real-time positioning of the mobile vehicle.

3. The road disease global detection system according to claim 2 is characterized in that: The GPS receiver includes a GPS antenna, which is arranged at the top center of the mobile vehicle.

4. The road disease global detection system according to claim 1 is characterized in that: The photographing device includes a high-definition camera, which is arranged in front of the mobile vehicle and is used to take pictures of the road surface in front while the mobile vehicle is traveling.

5. The road disease global detection system according to claim 1 is characterized in that: The laser scanning device includes a plurality of laser sensors, which are installed in front of the mobile vehicle in a linear arrangement. The plurality of laser sensors cooperate in front of the mobile vehicle to realize a laser point cloud detection area.

6. The road disease global detection system according to claim 1, characterized in that: The ground penetrating radar detection device comprises a plurality of single-channel dual-frequency ground penetrating radar antennas, and the plurality of single-channel dual-frequency ground penetrating radar antennas form a dual-frequency multi-channel radar detector.

7. The road disease global detection system according to claim 6, characterized in that: The dual-frequency ground penetrating radar antenna includes two pairs of butterfly-shaped dipoles, which correspond to a low-frequency antenna and a high-frequency antenna respectively. The main frequency of the low-frequency antenna is 100-400 MHz, and the main frequency of the high-frequency antenna is 400-900 MHz.

8. The road disease global detection system according to claim 1, characterized in that: The computer terminal includes a ground penetrating radar terminal for detecting underground road defects, a multi-point laser and video device terminal for detecting road surface defects, and a satellite map terminal for real-time display and collection of road locations: The ground penetrating radar detection terminal for underground road diseases is used to display and control the data of the ground penetrating radar detection device; the display includes: radar waveforms, two-dimensional profiles, and the location and type of road diseases collected by the ground penetrating radar detection device; the control includes: starting and stopping data collection and controlling the ground penetrating radar detection device; The multi-point laser and video device detection terminal for road surface damage is used to execute a road damage identification algorithm, or to obtain a real-time linear graph or a real-time curve graph formed by a laser scanning device, or to control a video device to take a road surface photo and synchronize the road surface photo with the position coordinates; The satellite map real-time display and collection road position terminal is used to control the positioning device to realize real-time observation and record the detailed position and current time of the detected road, and generate the position coordinates of the mobile vehicle.

9. The road disease global detection system according to claim 1, characterized in that: The road damage identification algorithm is specifically: Collect image data of road surface diseases, road internal structures and road internal diseases, and construct surface disease image datasets and underground structure and disease image datasets after processing the image data; A convolutional neural network model is constructed by using a convolutional neural network algorithm, and the convolutional neural network model is trained based on a surface disease image dataset and an underground structure and disease image dataset; Test and verify the trained model to obtain the road damage recognition algorithm.

10. A method for detecting road damage in the entire area, characterized in that: Using the road disease global detection system as described in any one of claims 1 to 9, the method steps are as follows: Start the mobile vehicle and use the positioning device to obtain the position coordinates of the mobile vehicle in real time; A laser scanning device is used to obtain laser scanning images, a photographing device is used to collect road surface photos, and a ground penetrating radar detection device is started to collect road radar images; Use road damage recognition algorithms to identify and delineate road surface and internal road damage in laser scanning images, road photos, and road radar images; Road surface defects and road internal defects are marked in combination with location coordinates to complete road defect detection.

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