System and method for detecting and analyzing road facility damage

Abstract

The invention relates to the technical field of image processing, target detection, visual large models and multi-terminal joint detection analysis, in particular to a system and method for detecting and analyzing road facility damage, and the system comprises a road facility anomaly recognition module which is used for carrying out the preliminary anomaly recognition of road facilities, and obtaining a suspected anomaly position; the road facility image acquisition module is used for acquiring a target angle image of a road facility corresponding to the suspected abnormal position; and the cloud anomaly recognition module is used for inputting the target angle image into a visual large model Agent for anomaly recognition again, and outputting an anomaly recognition result and spatial information of the anomaly. The road side camera and the large model technology are combined for conjoint analysis, road facility damage and damaged space information can be recognized in real time, maintenance and updating are conducted in time, and road driving safety is ensured.

Description

Technical Field

[0001] The present invention relates to the technical fields of image processing, target detection, large visual models and multi-terminal joint detection and analysis, and in particular to a system and method for detecting and analyzing damage to road facilities. Background Art

[0002] Road facilities refer to a series of structures and equipment set up to ensure traffic safety and improve traffic efficiency. These facilities play a vital role in traffic systems such as highways, urban roads and expressways. Common road facilities include: road signs (including warning signs, instruction signs, guide signs, etc.), road markings, traffic lights, guardrails, interception strips, traffic monitoring facilities, bridges, tunnels, etc. Road facilities play an important role in ensuring traffic safety, improving traffic efficiency, and improving the urban traffic environment. Reasonable planning and setting of road facilities can significantly improve the efficiency and safety of road use.

[0003] With the acceleration of urbanization and the increase in traffic volume, the number and complexity of road facilities are constantly increasing. Due to the influence of factors such as the natural environment, traffic flow, and heavy-load transportation, these facilities are constantly damaged and faulty due to overuse, wear and tear, and external impacts, and the maintenance of road facilities is becoming increasingly prominent. Existing technologies have not yet been able to effectively and timely monitor and evaluate the status of road facilities. For example, traditional inspection methods require professionals to conduct manual inspections of facilities on a regular basis, which is not only time-consuming and expensive, but also prone to missing potential problems. Recently, on-board cameras have been used in conjunction with detection algorithms to perform real-time damage detection of road facilities. However, due to factors such as occlusion by other vehicles, vehicle vibration, excessive speed, and weather interference (rainy days, strong light, etc.), the images collected by the on-board camera will be interfered with, thereby significantly reducing the accuracy of the detection, resulting in a large number of misidentifications, which require careful manual judgment.

[0004] Therefore, it is necessary to develop an effective technical solution to timely and accurately monitor and evaluate the damage status of road facilities and provide intelligent maintenance recommendations. Summary of the invention

[0005] The purpose of the present invention is to provide a system and method for detecting and analyzing damage to road facilities, which combines roadside cameras and large model technology for joint analysis, can identify damage to road facilities and spatial information of the damage in real time, and perform repairs and updates in a timely manner to ensure road driving safety.

[0006] To achieve the above objectives, on the one hand, the present invention provides a system for detecting and analyzing road facility damage, comprising:

[0007] The road facility anomaly recognition module is used to perform preliminary anomaly recognition on road facilities and obtain suspected anomaly locations;

[0008] A road facility image acquisition module, used to obtain target angle images of road facilities corresponding to suspected abnormal locations;

[0009] The cloud-based anomaly recognition module is used to input the target angle image into the visual large model Agent for re-anomaly recognition, and output the anomaly recognition result and the spatial information of the anomaly.

[0010] Optionally, the road facility anomaly identification module includes:

[0011] An anomaly recognition unit is used to collect road facility images in real time, use target detection algorithms to identify facility damage on road facility images, and obtain suspected anomalies;

[0012] The location determination unit is used to obtain the location information of the suspected anomaly by using the GPS matching stake number of the first image acquisition device corresponding to the road facility image.

[0013] Optionally, the road facility image acquisition module includes:

[0014] A second image acquisition device matching unit, configured to calculate and match the closest second image acquisition device based on the GPS corresponding to the suspected anomaly and the matched stake number;

[0015] A second image acquisition device setting unit, used to set the second image acquisition device parameters based on the stake number and the relative position of the matched second image acquisition device;

[0016] The second image acquisition device acquisition unit is used to photograph abnormal road facilities under the parameters of the second image acquisition device to obtain the target angle image.

[0017] Optionally, the second image acquisition device acquisition unit includes:

[0018] A first target angle image acquisition subunit, configured to photograph abnormal road facilities under the parameters of the second image acquisition device to acquire a first target angle image;

[0019] The second image acquisition device adjustment subunit is used to detect the starting and ending positions of the pixels of the abnormal road facilities in the first target angle image, calculate the initial preset frame, and adjust the parameters of the second image acquisition device based on the initial preset frame;

[0020] The second target angle image acquisition subunit is used to shoot again based on the adjusted second image acquisition device and output a second target angle image.

[0021] Optionally, the cloud-based anomaly recognition module includes a cloud-based anomaly recognition unit, which is used to input the second target angle image into the visual large model Agent, call the VLM for automatic recognition, and if there is a road facility abnormality, output the abnormality recognition result, the spatial information of the abnormality and the maintenance suggestion; if there is no road facility abnormality, identify the starting and ending points of the road facilities, calculate the preset frame, return to the second image acquisition device adjustment subunit to adjust the parameters of the second image acquisition device again, obtain a new image for re-recognition, and if the road facility abnormality is not identified until the preset termination recognition condition is reached, output no abnormality.

[0022] Optionally, the preset termination recognition condition includes: the longitudinal coordinate difference of the preset frame is less than Ht, and the pitch angle of the second image acquisition device reaches the maximum.

[0023] In another aspect, the present invention provides a method for detecting and analyzing road facility damage, comprising:

[0024] Conduct preliminary anomaly identification on road facilities and obtain suspected anomaly locations;

[0025] Obtain target angle images of road facilities corresponding to suspected abnormal locations;

[0026] The target angle image is input into the visual large model Agent for re-anomaly recognition, and the anomaly recognition result and the spatial information of the anomaly are output.

[0027] Optionally, preliminary anomaly identification of road facilities and obtaining suspected anomaly locations include:

[0028] Collect road facility images in real time, use target detection algorithms to identify facility damage and obtain suspected anomalies;

[0029] The GPS of the first image acquisition device corresponding to the road facility image is used to perform stake number matching to obtain location information of suspected anomalies.

[0030] Optionally, obtaining a target angle image of a road facility corresponding to the suspected abnormal position includes:

[0031] Based on the GPS corresponding to the suspected anomaly and the matched stake number, calculate and match the second image acquisition device that is closest to it;

[0032] Setting camera parameters based on the stake number and the relative position of the matched second image acquisition device;

[0033] photographing abnormal road facilities under the parameters of the second image acquisition device to obtain a first target angle image;

[0034] Detecting the starting and ending positions of the pixels of the abnormal road facilities in the first target angle image, calculating the initial preset frame, and adjusting the parameters of the second image acquisition device based on the initial preset frame;

[0035] Based on the adjusted second image acquisition device, shooting is performed again to output a second target angle image.

[0036] Optionally, the target angle image is input into the visual large model Agent for re-anomaly recognition, and the output of the anomaly recognition result and the spatial information of the anomaly includes:

[0037] The second target angle image is input into the visual large model Agent, and the VLM is called for automatic identification. If there is a road facility abnormality, the abnormality identification result, the spatial information of the abnormality and the maintenance suggestion are output; if there is no road facility abnormality, the starting and ending points of the road facility are identified, the preset frame is calculated, and the second image acquisition device adjustment subunit is returned to adjust the parameters of the second image acquisition device again, and a new image is obtained for re-identification. If the road facility abnormality is not identified until the preset termination recognition condition is reached, the output is no abnormality, wherein the preset termination recognition condition includes that the longitudinal coordinate difference of the preset frame is less than Ht and the pitch angle of the second image acquisition device reaches the maximum.

[0038] The beneficial effects of the present invention are:

[0039] (1) High precision: The use of vehicle-mounted camera target detection combined with multi-terminal cascade automatic analysis of roadside surveillance cameras can effectively improve recognition accuracy and reduce false alarms.

[0040] (2) High degree of automation: After the on-board camera detects and identifies facility damage, the system automatically calls nearby roadside cameras to capture the target location. Combined with the visual large model agent and recognition technology, it automatically calls the camera SDK to adjust the gimbal, then identifies facility damage in the image and gives specific damage type, damage location, and repair suggestions.

[0041] (3) Real-time monitoring: Through multi-terminal joint detection and analysis of large visual models, real-time monitoring can be achieved to detect facility damage in a timely manner, thereby reducing safety hazards and traffic accidents.

[0042] (4) Reduce maintenance costs: Through automated detection and intelligent analysis, the frequency and cost of manual inspections can be reduced, maintenance efficiency can be improved, and the service life of facilities can be extended. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments 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.

[0044] Figure 1 It is a layout diagram for implementing an embodiment of the present invention;

[0045] Figure 2 A system structure and workflow diagram for detecting and analyzing road facility damage according to an embodiment of the present invention;

[0046] Figure 3 This is a workflow diagram of the visual large model Agent according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] This embodiment is arranged as follows Figure 1 As shown, a system for detecting and analyzing road facility damage is provided, including a patrol vehicle equipped with an on-board camera and edge equipment, a roadside camera, and a cloud server. Figure 2 As shown, including:

[0050] The road facility anomaly recognition module is used to perform preliminary anomaly recognition on road facilities and obtain suspected anomaly locations;

[0051] A road facility image acquisition module, used to obtain target angle images of road facilities corresponding to suspected abnormal locations;

[0052] The cloud-based anomaly recognition module is used to input the target angle image into the visual large model Agent for re-anomaly recognition, and output the anomaly recognition result and the spatial information of the anomaly.

[0053] Specifically, the function of the road facility abnormality recognition module of this embodiment is as follows: Figure 1 The inspection vehicle equipped with on-board cameras and edge devices is realized, and the function of the road facility image acquisition module is realized through Figure 1The middle road side camera is realized, and the cloud abnormality recognition module functions through Figure 1 Implemented by Zhongyun Server.

[0054] This embodiment uses on-board camera target detection, combined with multi-terminal cascade automatic analysis of roadside monitoring cameras, which can effectively improve recognition accuracy and reduce false alarms; after the on-board camera of this embodiment detects and identifies facility damage, the system automatically calls nearby roadside cameras to capture the target position, combines the visual big model Agent and recognition technology, automatically calls the camera SDK to adjust the pan / tilt, and then identifies the facility damage and the spatial information of the damage in the image, and gives specific damage types and repair suggestions, etc.; through multi-terminal joint detection and analysis of the visual big model, real-time monitoring can be achieved, facility damage can be discovered in time, and safety hazards and traffic accidents can be reduced; through automated detection and intelligent analysis, the frequency and cost of manual inspections can be reduced, maintenance efficiency can be improved, and the service life of facilities can be extended.

[0055] Furthermore, the road facility anomaly recognition module includes:

[0056] An anomaly recognition unit is used to collect road facility images in real time, use target detection algorithms to identify facility damage on road facility images, and obtain suspected anomalies;

[0057] The location determination unit is used to use the GPS matching stake number of the first image acquisition device corresponding to the road facility image to obtain the location information of the suspected anomaly.

[0058] Specifically, in this embodiment, target detection is used on the edge device to identify in real time whether there are abnormalities in road facilities, and the pile number is matched according to the GPS of the image acquisition device. The specific operation of the road facility abnormality identification module is as follows:

[0059] Step 1.1, identify anomalies in real time;

[0060] The on-board camera of the patrol vehicle collects real-time images Img_C of road facilities. The edge device uses a target detection algorithm for facility damage identification, including but not limited to the YOLO-V8 algorithm model, to perform real-time detection and identify abnormalities or damage on the Img_C image.

[0061] Step 1.2, calculate the position and pile number matching;

[0062] If step 1.1 identifies an abnormality in the current image, the stake number such as K1+100 is matched according to the GPS of the image acquisition device.

[0063] This module will output whether there is an abnormality and the abnormal location description. For example: On the uphill section of K1+100, the guardrail on the left side of the road is suspected to be damaged.

[0064] Furthermore, the road facility image acquisition module includes:

[0065] A second image acquisition device matching unit, configured to calculate and match the closest second image acquisition device based on the GPS corresponding to the suspected anomaly and the matched stake number;

[0066] A second image acquisition device setting unit, used to set the second image acquisition device parameters based on the stake number and the relative position of the matched second image acquisition device;

[0067] The second image acquisition device acquisition unit is used to photograph abnormal road facilities under the second image acquisition device parameters to obtain target angle images.

[0068] Wherein, the second image acquisition device acquisition unit includes:

[0069] A first target angle image acquisition subunit, used to photograph abnormal road facilities under the second image acquisition device parameters to acquire a first target angle image;

[0070] The second image acquisition device adjustment subunit is used to detect the starting and ending positions of the pixels of the abnormal road facilities in the first target angle image, calculate the initial preset frame, and adjust the parameters of the second image acquisition device based on the initial preset frame;

[0071] The second target angle image acquisition subunit is used to shoot again based on the adjusted second image acquisition device and output a second target angle image.

[0072] Specifically, in this embodiment, the roadside camera has a 3D positioning function, and inputs a pixel area position in the roadside image Img_R, so that the area can be moved to the center of the video, and then zoomed in or out, and the camera's pitch angle and horizontal angle are rotated according to the relative position relationship between the center of the area position and the center of the roadside image Img_R. The specific workflow of the road facility image acquisition module is as follows:

[0073] Step 2.1, matching nearby roadside cameras;

[0074] According to the GPS and stake number calculated by the road facility anomaly recognition module, the nearest roadside monitoring camera is calculated.

[0075] Step 2.2, initial state setting of roadside camera;

[0076] After matching the pile number information in step 1.2 and matching the nearby roadside camera in step 2.1, call the camera SDK based on the approximate relative position of the two, preliminarily adjust the angle of the roadside camera, specifically adjust the pitch angle to the lowest, adjust the focal length to the smallest, and collect the current roadside camera image Img_R.

[0077] Calling the facility key point detection algorithm includes but is not limited to YOLOV8-POSE, detecting the starting and ending positions of the pixel points of the facilities in the roadside camera image Img_R, the starting points are [x0, y0] and [x1, y1] below the image, and calculating the initial preset box (represented by the upper left point and the lower right point, [xmin, ymin, xmax, ymax]) as [x1, y1, x0, y0].

[0078] Step 2.3, initial view image of the roadside camera;

[0079] Input the initial preset frame obtained in step 2.2 into the PTZ control 3D positioning in the camera SDK, and the roadside camera will move the preset frame area to the center of the image Img_R and zoom in. At this time, the screen will focus on the target facility and output the roadside camera image Img_R collected at this time.

[0080] Furthermore, the cloud-based anomaly recognition module includes a cloud-based anomaly recognition unit, which is used to input the second target angle image into the visual large model Agent, call the VLM for automatic recognition, and output the anomaly recognition result, spatial information of the anomaly and maintenance suggestions if there is a road facility anomaly; if there is no road facility anomaly, identify the starting and ending points of the road facilities, calculate the preset frame, return to the second image acquisition device adjustment subunit to adjust the parameters of the second image acquisition device again, obtain a new image for re-recognition, and output no anomaly if the road facility anomaly is not identified until the preset termination recognition condition is reached.

[0081] The preset termination recognition conditions include: the longitudinal coordinate difference of the preset frame is less than Ht, and the pitch angle of the second image acquisition device reaches the maximum.

[0082] Specifically, in this embodiment, the cloud anomaly recognition module receives the roadside image Img_R taken by the roadside camera at the initial angle obtained in step 2.3, calls VLM for automatic recognition, and analyzes and performs subsequent operations. Facilities such as guardrails are generally long, and it is necessary to control the camera to scan along the guardrail. The scanning will magnify the target and gradually extend to the distance, so as to identify whether there is really an anomaly and the spatial information where the anomaly is located. The specific workflow is as follows:

[0083] Step 3.1, Visual large model Agent;

[0084] Determine whether there is any abnormality in the current roadside camera image Img_R and the spatial information of the abnormality. If not, get the next preset frame, call the roadside camera SDK, enter the preset frame, get the camera adjusted image, repeat this process, and complete the zoom scan and abnormality recognition of the facility. Figure 3 shown.

[0085] The visual model agent contains three functions:

[0086] A. Use tool 1 to obtain the location of the facility. The input is the roadside camera image Img_R. Call the facility key point detection to identify the start and end key points of the facility in the input image, calculate the start and end points of the facility in the input image, and return the start and end points.

[0087] B. Use tool 2 to calculate the preset frame. The input is the starting and ending points of the facility. Recalculate the next preset frame. The specific calculation method is:

[0088] a) The horizontal xmin is the minimum horizontal value of the starting and ending points of the facility.

[0089] b) The horizontal xmax is the maximum horizontal value of the starting and ending points of the facility.

[0090] c) Longitudinal ymin is the longitudinal coordinate of the starting point of the facility -Hs, where Hs is 100 by default.

[0091] d) Longitudinal ymax takes the longitudinal coordinate of the end point of the facility.

[0092] Returns the calculated preset frame. The center point of this preset frame is usually higher than the center point of the image, which will guide the camera to raise the pitch angle and extend the shooting facilities.

[0093] C. Use tool 3 to obtain the adjusted roadside image, input the preset frame, call the roadside camera SDK, configure the preset frame into the 3D control in the SDK, and the roadside camera will automatically adjust and return the roadside camera image Img_R taken after adjustment.

[0094] Task: Please pay attention to the road facilities in the image and determine whether there is any damage. The judgment is as follows:

[0095] 1) If the damage cannot be effectively identified, call A to obtain the start and end points of the facility, then call B to obtain the preset frame, and finally call C to obtain the adjusted roadside image and repeat this step.

[0096] 2) If damage is found, confirm that there is indeed damage here, output the damage, and record the name of the currently damaged facility, damage type, damage location (including spatial information, such as above and below the elevated road, above and below the ramp, etc.) and repair suggestions.

[0097] 3) Repeat the above steps 1) and 2) and terminate if any of the following three conditions are met:

[0098] Termination condition 1: Damage is found.

[0099] Termination condition 2: If the difference between the longitudinal coordinates of the preset frame is less than Ht, which is 200 by default, it is determined that this section of the facility has been scanned to the end.

[0100] Termination condition 3: The pitch angle of the roadside camera reaches the maximum.

[0101] If termination condition 1 and termination condition 2 are met and no damage is found when the adjustment end point is reached, the output is that there is no abnormal damage here.

[0102] Step 3.2, loop call analysis;

[0103] When damage is found in step 3.1, set the interval of 20s to call execution module 3, and call it five times in a row.

[0104] The results of these five returns are comprehensively analyzed. If any of them show damaged facilities, it is determined that there are damaged facilities in the current image area. This can determine whether there are actually damaged facilities in the current image, the specific name and type of damaged facilities, the location of the damage, and repair suggestions. This can then be fed back to the road administration department in a timely manner for confirmation and repair.

[0105] On the other hand, this embodiment provides a method for detecting and analyzing road facility damage, including:

[0106] Conduct preliminary anomaly identification on road facilities and obtain suspected anomaly locations;

[0107] Obtain target angle images of road facilities corresponding to suspected abnormal locations;

[0108] The target angle image is input into the visual large model Agent for anomaly recognition again, and the anomaly recognition result and the spatial information of the anomaly are output.

[0109] Furthermore, preliminary abnormality identification is performed on road facilities to obtain suspected abnormal locations, including:

[0110] Collect road facility images in real time, use target detection algorithms to identify facility damage and obtain suspected anomalies;

[0111] The GPS of the first image acquisition device corresponding to the road facility image is used to perform stake number matching to obtain location information of suspected anomalies.

[0112] Furthermore, obtaining a target angle image of a road facility corresponding to a suspected abnormal position includes:

[0113] Based on the GPS corresponding to the suspected anomaly and the matched stake number, calculate and match the second image acquisition device that is closest to it;

[0114] Setting camera parameters based on the stake number and the relative position of the matched second image acquisition device;

[0115] photographing abnormal road facilities under the parameters of the second image acquisition device to obtain a first target angle image;

[0116] Detecting the starting and ending positions of the pixels of the abnormal road facilities in the first target angle image, calculating the initial preset frame, and adjusting the parameters of the second image acquisition device based on the initial preset frame;

[0117] Based on the adjusted second image acquisition device, shooting is performed again to output a second target angle image.

[0118] Furthermore, the target angle image is input into the visual large model Agent for another abnormality recognition, and the output abnormality recognition result and the spatial information of the abnormality include:

[0119] The second target angle image is input into the visual large model Agent, and the VLM is called for automatic identification. If there is a road facility abnormality, the abnormality identification result, the spatial information of the abnormality and the maintenance suggestion are output; if there is no road facility abnormality, the starting and ending points of the road facility are identified, the preset frame is calculated, and the second image acquisition device adjustment subunit is returned to adjust the parameters of the second image acquisition device again, and a new image is obtained for re-identification. If the road facility abnormality is not identified until the preset termination recognition condition is reached, the output is no abnormality, wherein the preset termination recognition condition includes that the longitudinal coordinate difference of the preset frame is less than Ht and the pitch angle of the second image acquisition device reaches the maximum.

[0120] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A system for detecting and analyzing road facility damage, characterized in that: include: The road facility anomaly identification module is used to perform preliminary anomaly identification on road facilities and obtain suspected anomaly locations; A road facility image acquisition module, used to obtain target angle images of road facilities corresponding to suspected abnormal locations; The cloud-based anomaly recognition module is used to input the target angle image into the visual large model Agent for re-anomaly recognition, and output the anomaly recognition result and the spatial information of the anomaly.

2. The system for detecting and analyzing road facility damage according to claim 1, characterized in that: The road facility abnormality recognition module includes: An anomaly recognition unit is used to collect road facility images in real time, use target detection algorithms to identify facility damage on road facility images, and obtain suspected anomalies; The location determination unit is used to obtain the location information of the suspected anomaly by using the GPS matching stake number of the first image acquisition device corresponding to the road facility image.

3. The system for detecting and analyzing road facility damage according to claim 1, characterized in that: The road facility image acquisition module comprises: A second image acquisition device matching unit, configured to calculate and match the closest second image acquisition device based on the GPS corresponding to the suspected anomaly and the matched stake number; A second image acquisition device setting unit, used to set the second image acquisition device parameters based on the stake number and the relative position of the matched second image acquisition device; The second image acquisition device acquisition unit is used to photograph abnormal road facilities under the parameters of the second image acquisition device to obtain the target angle image.

4. The system for detecting and analyzing road facility damage according to claim 3, characterized in that: The second image acquisition device acquisition unit comprises: A first target angle image acquisition subunit, configured to photograph abnormal road facilities under the parameters of the second image acquisition device to acquire a first target angle image; The second image acquisition device adjustment subunit is used to detect the starting and ending positions of the pixels of the abnormal road facilities in the first target angle image, calculate the initial preset frame, and adjust the parameters of the second image acquisition device based on the initial preset frame; The second target angle image acquisition subunit is used to shoot again based on the adjusted second image acquisition device and output a second target angle image.

5. The system for detecting and analyzing road facility damage according to claim 1, characterized in that: The cloud-based anomaly recognition module includes a cloud-based anomaly recognition unit, which is used to input the second target angle image into the visual large model Agent, call the VLM for automatic recognition, and output the anomaly recognition result, spatial information of the anomaly and maintenance suggestions if there is a road facility anomaly; if there is no road facility anomaly, identify the starting and ending points of the road facility, calculate the preset frame, return to the second image acquisition device adjustment subunit to adjust the parameters of the second image acquisition device again, obtain a new image for re-recognition, and output no anomaly if the road facility anomaly is not recognized until the preset termination recognition condition is reached.

6. The system for detecting and analyzing road facility damage according to claim 5, characterized in that: The preset termination recognition conditions include: the longitudinal coordinate difference of the preset frame is less than Ht, and the pitch angle of the second image acquisition device reaches the maximum.

7. A method for detecting and analyzing road facility damage, characterized in that: include: Conduct preliminary anomaly identification on road facilities and obtain suspected anomaly locations; Acquire target angle images of road facilities corresponding to suspected abnormal locations; The target angle image is input into the visual large model Agent for re-anomaly recognition, and the anomaly recognition result and the spatial information of the anomaly are output.

8. The method for detecting and analyzing road facility damage according to claim 7, characterized in that: Perform preliminary anomaly identification on road facilities and obtain suspected anomaly locations, including: Collect road facility images in real time, use target detection algorithms to identify facility damage and obtain suspected anomalies; The GPS of the first image acquisition device corresponding to the road facility image is used to perform stake number matching to obtain location information of suspected anomalies.

9. The method for detecting and analyzing road facility damage according to claim 7, characterized in that: Obtaining target angle images of road facilities corresponding to suspected abnormal locations includes: Based on the GPS corresponding to the suspected anomaly and the matched stake number, calculate and match the second image acquisition device that is closest to it; Setting camera parameters based on the stake number and the relative position of the matched second image acquisition device; photographing abnormal road facilities under the parameters of the second image acquisition device to obtain a first target angle image; Detecting the starting and ending positions of the pixels of the abnormal road facilities in the first target angle image, calculating the initial preset frame, and adjusting the parameters of the second image acquisition device based on the initial preset frame; Based on the adjusted second image acquisition device, shooting is performed again to output a second target angle image.

10. The method for detecting and analyzing road facility damage according to claim 7, characterized in that: The target angle image is input into the visual large model Agent for re-anomaly recognition, and the output anomaly recognition result and the spatial information of the anomaly include: The second target angle image is input into the visual large model Agent, and the VLM is called for automatic identification. If there is a road facility abnormality, the abnormality identification result, the spatial information of the abnormality and the maintenance suggestion are output; if there is no road facility abnormality, the starting and ending points of the road facility are identified, the preset frame is calculated, and the second image acquisition device adjustment subunit is returned to adjust the parameters of the second image acquisition device again, and a new image is obtained for re-identification. If the road facility abnormality is not identified until the preset termination recognition condition is reached, the output is no abnormality, wherein the preset termination recognition condition includes that the longitudinal coordinate difference of the preset frame is less than Ht and the pitch angle of the second image acquisition device reaches the maximum.

Images