Bridge disease monitoring method based on unmanned aerial vehicle

Abstract

The invention relates to a bridge disease monitoring method based on an unmanned aerial vehicle, and relates to the field of bridge monitoring, and the method comprises the steps: obtaining the actual bridge surface image information, sent by an image collection device, of a bridge monitoring region in real time according to a preset image obtaining path; analyzing the actual direction of the actual bridge surface image information in the bridge to obtain an actual direction bridge picture; querying an initial bridge picture corresponding to the actual orientation in a preset standard bridge picture database; judging whether the actual bridge picture is consistent with the standard bridge picture or not; if not, identifying an inconsistent area on the actual bridge picture and the standard bridge picture; marking an inconsistent area on the actual bridge picture, and setting the inconsistent area as a disease area; and sending the actual bridge picture with the marked disease area to a background management platform for display. According to the invention, the unmanned aerial vehicle replaces manual operation to carry out disease detection on the bridge, so that the detection precision and efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of bridge monitoring, and in particular to a bridge defect monitoring method based on drones. Background Art

[0002] In recent years, with economic development and increased traffic volume, the number of oversized and overweight vehicles has increased, placing higher demands on the capacity and load-bearing capacity of transportation facilities such as bridges. Due to improper maintenance and severe overloading, some older beams have become deteriorating and damaged, making them unable to meet traffic demand. To ensure smooth traffic and safe driving, it is imperative to strengthen routine bridge safety monitoring.

[0003] Cracks are the most common early-stage hazard in reinforced concrete bridges. Timely crack detection is crucial for reducing bridge damage. Traditional contact inspection methods require expensive bridge inspection vehicles, resulting in long inspection cycles and high costs. Manual operation is also risky, and measurement results are subject to subjectivity and random errors, compromising the accuracy of bridge damage detection. Summary of the Invention

[0004] In order to improve the accuracy and efficiency of bridge defect detection, this application provides a bridge defect monitoring method based on drones.

[0005] In the first aspect, the present application provides a method for monitoring bridge defects based on drones using the following technical solutions:

[0006] According to the preset image acquisition path, the actual bridge surface image information of the bridge monitoring area sent by the image acquisition device is acquired in real time;

[0007] Analyze the actual bridge surface image information to locate the actual position of the bridge to obtain a picture of the bridge at the actual position;

[0008] Searching a preset standard bridge image database for an initial bridge image corresponding to the actual position;

[0009] Determining whether the actual bridge image and the standard bridge image are consistent; if not, identifying inconsistent areas between the actual bridge image and the standard bridge image;

[0010] Marking inconsistent areas on the actual bridge image and setting the inconsistent areas as defective areas;

[0011] The actual bridge pictures with the areas marked as damaged are sent to the backend management platform for display.

[0012] Optionally, the actual bridge surface image information includes an actual bridge surface picture and its corresponding shooting path number, and analyzing the actual bridge surface image information to locate the actual position of the bridge to obtain the actual position bridge picture includes:

[0013] Querying a preset location database for the actual location corresponding to the path number;

[0014] The actual position is marked on the actual bridge surface image to obtain the actual bridge image.

[0015] Optionally, the actual bridge surface image information also includes geographical location information of the monitored bridge; after sending the actual bridge image with the area marked as damaged to the backend management platform, the method further includes:

[0016] Analyzing the types of bridge defects in the defective area in the actual bridge image;

[0017] Searching a preset bridge maintenance reminder database for contact information of maintenance personnel corresponding to the bridge disease type;

[0018] Send the bridge damage type and the corresponding maintenance personnel contact information to the backend management platform;

[0019] According to the maintenance personnel's contact information, relevant information such as the geographical location information of the monitored bridge, the type of bridge disease, etc. is sent to the maintenance personnel's mobile device.

[0020] Optionally, after obtaining the actual bridge position picture, the method further includes:

[0021] According to the preset bridge orientation sequence and the actual orientation, a BIM system is used to process data of a number of actual bridge images to construct a three-dimensional bridge model;

[0022] According to the defective areas in the actual bridge image, marking the defective areas and their corresponding types of defects on the three-dimensional bridge model to obtain a three-dimensional bridge defect model;

[0023] The three-dimensional model of the bridge damage is sent to the background management platform for display.

[0024] Optionally, analyzing the bridge damage type of the damaged area in the actual bridge image includes:

[0025] Calculating the actual diseased area of ​​the actual diseased area;

[0026] Retrieving a standard disease area range corresponding to the actual disease area from a preset disease type database;

[0027] If the number of the standard disease area ranges is unique, then retrieve the first standard disease type corresponding to the standard disease area range from a preset disease type database;

[0028] The first standard damage type is set as the actual damage type of the bridge.

[0029] Optionally, after retrieving the standard disease range corresponding to the actual disease area from the preset disease type database, the method further includes:

[0030] If the number of the quasi-disease ranges is not unique, a second standard disease type corresponding to each standard disease area range is retrieved from a preset disease type database;

[0031] Searching a preset disease category database for standard disease images corresponding to the second standard disease category;

[0032] intercepting a picture of an actual damaged area in the actual bridge picture;

[0033] Performing a similarity comparison between the actual diseased area image and the standard diseased area image to obtain an actual similarity value;

[0034] Determine whether the actual similarity value is within a preset similarity standard range; if so, retrieve a second standard disease category corresponding to the standard disease image from a preset disease category database;

[0035] The second standard damage type is set as the actual damage type of the bridge.

[0036] Optionally, after determining whether the actual similarity value is within a preset similarity standard range, the method further includes:

[0037] If the actual similarity value is outside the preset similarity standard range, a prompt message related to the disease type confirmation with the actual disease area picture is sent to the background management platform;

[0038] Receiving the third standard disease type sent back by the backend management platform;

[0039] The third standard damage type is set as the actual damage type of the bridge.

[0040] In a second aspect, the present application provides a bridge defect monitoring system based on a drone, the system comprising:

[0041] An image acquisition unit is used to acquire, in real time, image information of the actual bridge surface in the bridge monitoring area sent by the image acquisition device according to a preset image acquisition path;

[0042] An orientation analysis unit is used to analyze the actual orientation of the bridge based on the actual bridge surface image information, and obtain a bridge image at the actual orientation;

[0043] A picture query unit, configured to query a preset standard bridge picture database for an initial bridge picture corresponding to the actual position;

[0044] a region identification unit, configured to determine whether the actual bridge image and the standard bridge image are consistent; if not, identifying inconsistent regions between the actual bridge image and the standard bridge image;

[0045] a defect determination unit, configured to mark inconsistent regions on the actual bridge image and set the inconsistent regions as defect regions;

[0046] The defect display unit is used to send actual bridge pictures with areas marked as defective to the backend management platform for display.

[0047] In a third aspect, the present application provides a computer device that adopts the following technical solution: it includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute any of the above-mentioned drone-based bridge disease monitoring methods.

[0048] In a fourth aspect, the present application provides a storage medium that adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned drone-based bridge defect monitoring methods.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] 1. Using drones instead of manual bridge photography saves time and effort, and helps improve the accuracy and efficiency of bridge defect monitoring;

[0051] 2. After the system obtains the bridge image, it analyzes the type of bridge damage and sends the type of bridge damage to the maintenance personnel's communication equipment, so that the maintenance personnel can take corresponding measures to repair the bridge damage, which helps to improve the efficiency of bridge damage repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a bridge defect monitoring method based on drones in an embodiment of the present application.

[0053] Figure 2 This is a system block diagram of a bridge defect monitoring method based on drones in an embodiment of the present application.

[0054] Explanation of the accompanying symbols: 201, image acquisition unit; 202, orientation analysis unit; 203, image query unit; 204, region recognition unit; 205, disease determination unit; 206, disease display unit. DETAILED DESCRIPTION

[0055] The following is combined with Figure 1-2 This application is described in further detail.

[0056] The embodiment of the present application discloses a method for monitoring bridge defects based on drones. The method is based on a backend management platform for monitoring bridge defects based on drones. The backend management platform can be a computer / laptop, iPad, or other mobile device. A backend management system is provided in the backend management platform. A drone flight route database is pre-established in the backend management system. The drone flight route database stores a number of bridge numbers, the bridge address corresponding to each bridge number, and the drone flight path corresponding to each bridge number. Staff can control the drone to reach a designated address through the backend management platform and fly according to the flight route corresponding to the bridge number. In order to replace manual monitoring of bridge defects, the drone is equipped with a camera to monitor bridge defects.

[0057] like Figure 1 As shown, the method includes the following steps:

[0058] S101, according to a preset image acquisition path, real-time acquisition of actual bridge surface image information of the bridge monitoring area sent by the image acquisition device;

[0059] S102, analyzing the actual bridge surface image information to determine the actual position of the bridge, and obtaining a picture of the bridge at the actual position;

[0060] S103, searching a preset standard bridge image database for an initial bridge image corresponding to the actual position;

[0061] S104, determining whether the actual bridge image is consistent with the standard bridge image;

[0062] S105, if not, identifying inconsistent areas between the actual bridge image and the standard bridge image;

[0063] S106, marking inconsistent areas on the actual bridge image, and setting the inconsistent areas as defective areas;

[0064] S107, sending the actual bridge picture with the damaged area marked to the backend management platform for display.

[0065] It should be noted that staff members issue bridge photography commands to drones through the backend management platform. Upon receiving the command, the drones follow a pre-set image acquisition path to capture real-time images of the bridge surface, obtain actual bridge surface image information, and transmit the captured actual bridge surface image information to the backend management platform in real time. In this embodiment, the actual bridge surface image information includes the actual bridge surface image, the corresponding photography path number, and the geographical location of the monitored bridge.

[0066] After receiving the information, the system analyzes the actual bridge surface image information and determines its actual location on the bridge, thereby obtaining an image of the bridge at that location. The system also pre-establishes a bridge information database, which includes multiple bridge numbers, multiple actual locations corresponding to each bridge number, and an initial bridge image corresponding to each actual location. The initial bridge image is an image of a bridge without any damage. The system can then query the standard bridge image database for the initial bridge image corresponding to the actual location. If the system determines that the actual bridge image and the standard bridge image are inconsistent, the system identifies the inconsistent areas between the actual bridge image and the standard bridge image, marks the inconsistent areas on the actual bridge image, and sets the inconsistent areas as defective areas.

[0067] Finally, the system sends actual bridge images with marked damaged areas to the backend management platform for display. This allows staff to view bridge images in real time, analyze whether the bridge has developed any damage, and facilitate timely repairs. The bridge damage monitoring method constructed using the above scheme replaces manual acquisition of bridge images, saving time and effort and helping to improve the accuracy and efficiency of bridge damage monitoring.

[0068] In one embodiment, in order to facilitate the system to quickly obtain and confirm that the bridge image taken by the drone is the specific location of the bridge, the actual bridge surface image information is analyzed to be located at the actual location of the bridge, and the actual location bridge image is obtained by performing the following steps:

[0069] Querying the actual location corresponding to the path number in a preset location database;

[0070] The actual position is marked on the actual bridge surface image to obtain the actual bridge image.

[0071] In one embodiment, in order to facilitate maintenance personnel to repair the damaged areas of the bridge as quickly as possible, after sending the actual bridge picture with the damaged areas marked to the backend management platform, the following measures can also be taken:

[0072] Analyze the types of bridge defects in the defective areas of actual bridge images;

[0073] Query the contact information of maintenance personnel corresponding to the type of bridge disease in the preset bridge maintenance reminder database;

[0074] Send the bridge damage types and corresponding maintenance personnel contact information to the backend management platform;

[0075] According to the maintenance personnel's contact information, relevant information such as the geographical location of the monitored bridge, the type of bridge disease, etc. is sent to the maintenance personnel's mobile device.

[0076] In one embodiment, in order to facilitate workers to quickly obtain the types of bridge defects, the following operations may be performed to analyze the types of bridge defects in the defective areas in the actual bridge images:

[0077] Calculate the actual diseased area in the actual diseased area;

[0078] Retrieve the standard disease area range corresponding to the actual disease area from the preset disease type database;

[0079] If the number of standard disease area ranges is unique, the first standard disease type corresponding to the standard disease area range is retrieved from a preset disease type database;

[0080] The first standard damage type is set to the actual damage type of the bridge.

[0081] In one embodiment, in order to further improve the accuracy of the system in determining the disease type, after retrieving the standard disease range corresponding to the actual disease area from the preset disease type database, the following operations may be performed:

[0082] If the number of quasi-disease ranges is not unique, the second standard disease type corresponding to each standard disease area range is retrieved from the preset disease type database;

[0083] Searching for standard disease pictures corresponding to the second standard disease type in a preset disease type database;

[0084] Capture pictures of actual damaged areas from actual bridge pictures;

[0085] Compare the similarity between the actual diseased area picture and the standard diseased picture to obtain the actual similarity value;

[0086] Determine whether the actual similarity value is within a preset similarity standard range; if so, retrieve a second standard disease category corresponding to the standard disease image from a preset disease category database;

[0087] The second standard damage type is set to the actual damage type of the bridge.

[0088] In one embodiment, considering the possibility of misjudgment by the system, after determining whether the actual similarity value is within the preset similarity standard range, the following steps may be further performed:

[0089] If the actual similarity value is outside the preset similarity standard range, a prompt message confirming the disease type with a picture of the actual diseased area will be sent to the backend management platform;

[0090] Receive the third standard disease type sent back by the backend management platform;

[0091] The third standard damage type is set to the actual damage type of the bridge.

[0092] Based on the above method, an embodiment of the present application also discloses a bridge disease monitoring system based on a drone.

[0093] Combine Figure 2 , the system includes the following units:

[0094] The image acquisition unit 201 is used to acquire the actual bridge surface image information of the bridge monitoring area sent by the image acquisition device in real time according to a preset image acquisition path;

[0095] The orientation analysis unit 202 is used to analyze the actual orientation of the bridge based on the actual bridge surface image information to obtain a bridge image at the actual orientation;

[0096] The picture query unit 203 is used to query the initial bridge picture corresponding to the actual position in the preset standard bridge picture database;

[0097] The region identification unit 204 is used to determine whether the actual bridge image and the standard bridge image are consistent; if not, to identify inconsistent regions between the actual bridge image and the standard bridge image;

[0098] a defect determination unit 205, configured to mark inconsistent areas on the actual bridge image and set the inconsistent areas as defect areas;

[0099] The damage display unit 206 is used to send the actual bridge picture with the damaged area marked to the background management platform for display.

[0100] In one embodiment, the actual bridge surface image information includes an actual bridge surface picture and its corresponding shooting path number. The actual bridge surface image information is analyzed to determine the actual orientation of the bridge to obtain the actual orientation bridge picture. The orientation analysis unit specifically includes:

[0101] The actual orientation corresponding to the path number is searched in a preset orientation database; the actual orientation is marked on the actual bridge surface image to obtain the actual bridge image.

[0102] In one embodiment, the actual bridge surface image information also includes the geographical location information of the monitored bridge; after the actual bridge picture with the area marked as a diseased area is sent to the background management platform, the system also includes a mobile communication unit for analyzing the type of bridge disease in the diseased area in the actual bridge picture; querying the contact information of the maintenance personnel corresponding to the type of bridge disease in the preset bridge maintenance reminder database; sending the type of bridge disease and the corresponding contact information of the maintenance personnel to the background management platform; according to the contact information of the maintenance personnel, sending relevant information such as the geographical location information of the monitored bridge, the type of bridge disease, etc. to the mobile device of the maintenance personnel.

[0103] In one embodiment, after obtaining the actual bridge orientation picture, the system also includes a bridge model display unit, which is used to use the BIM system to process data of several actual bridge pictures according to the preset bridge orientation sequence and actual orientation to construct a three-dimensional bridge model; according to the defective areas in the actual bridge pictures, the defective areas and their corresponding types of defects are marked on the three-dimensional bridge model to obtain a three-dimensional bridge defect model; and the three-dimensional bridge defect model is sent to the background management platform for display.

[0104] In one embodiment, the bridge defect types in the defect area in the actual bridge image are analyzed, and the system also includes a defect type analysis unit for calculating the actual defect area of ​​the actual defect area; retrieving the standard defect area range corresponding to the actual defect area from a preset defect type database; if the number of standard defect area ranges is unique, retrieving the first standard defect type corresponding to the standard defect area range from the preset defect type database; and setting the first standard defect type as the actual bridge defect type.

[0105] In one embodiment, after retrieving the standard disease range corresponding to the actual disease area from the preset disease type database, the disease type analysis unit is further used to: if the number of quasi-disease ranges is not unique, then retrieve the second standard disease type corresponding to each standard disease area range from the preset disease type database; query the standard disease picture of the second standard disease type in the preset disease type database; intercept the actual disease area picture in the actual bridge picture; perform a similarity comparison between the actual disease area picture and the standard disease picture to obtain an actual similarity value; determine whether the actual similarity value is within the preset similarity standard range; if so, retrieve the second standard disease type corresponding to the standard disease picture from the preset disease type database; and set the second standard disease type as the actual bridge disease type.

[0106] In one embodiment, after determining whether the actual similarity value is within the preset similarity standard range, the defect type analysis unit is further used to: if the actual similarity value is outside the preset similarity standard range, send a prompt message related to the confirmation of the defect type with a picture of the actual defect area to the background management platform; receive the third standard defect type returned by the background management platform; and set the third standard defect type as the actual defect type of the bridge.

[0107] The embodiment of the present application also discloses a computer device.

[0108] Specifically, the device includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed by the above-mentioned UAV-based bridge defect monitoring method. The present application also discloses a computer-readable storage medium.

[0109] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed by a method for monitoring bridge defects based on a drone. The computer-readable storage medium includes, for example, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0110] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A bridge disease monitoring method based on drone, characterized in that: The following steps are involved: According to the preset image acquisition path, the actual bridge surface image information of the bridge monitoring area sent by the image acquisition device is acquired in real time; Analyze the actual bridge surface image information to locate the actual position of the bridge to obtain a picture of the bridge at the actual position; Searching a preset standard bridge image database for an initial bridge image corresponding to the actual position; Determining whether the actual bridge image is consistent with the standard bridge image; If not, identifying inconsistent areas between the actual bridge image and the standard bridge image; Marking inconsistent areas on the actual bridge image and setting the inconsistent areas as defective areas; The actual bridge pictures with the areas marked as damaged are sent to the backend management platform for display.

2. The method according to claim 1, characterized in that The actual bridge surface image information includes an actual bridge surface picture and its corresponding shooting path number. The analyzing the actual bridge surface image information to locate the actual position of the bridge to obtain the actual position bridge picture includes: Querying a preset location database for the actual location corresponding to the path number; The actual position is marked on the actual bridge surface image to obtain the actual bridge image.

3. The method according to claim 2, characterized in that The actual bridge surface image information also includes geographical location information of the monitored bridge; After sending the actual bridge picture with the damaged area marked to the backend management platform, the method further includes: Analyzing the types of bridge defects in the defective area in the actual bridge image; Searching a preset bridge maintenance reminder database for contact information of maintenance personnel corresponding to the bridge disease type; Send the bridge damage type and the corresponding maintenance personnel contact information to the backend management platform; According to the maintenance personnel's contact information, relevant information such as the geographical location information of the monitored bridge, the type of bridge disease, etc. is sent to the maintenance personnel's mobile device.

4. The method according to claim 3, characterized in that After obtaining the actual bridge position picture, the method further includes: According to the preset bridge orientation sequence and the actual orientation, a BIM system is used to process data of a number of actual bridge images to construct a three-dimensional bridge model; According to the defective areas in the actual bridge image, marking the defective areas and their corresponding types of defects on the three-dimensional bridge model to obtain a three-dimensional bridge defect model; The three-dimensional model of the bridge damage is sent to the background management platform for display.

5. The method according to claim 3, characterized in that The analyzing the bridge defect types in the defect area in the actual bridge image includes: Calculating the actual diseased area of ​​the actual diseased area; Retrieving a standard disease area range corresponding to the actual disease area from a preset disease type database; If the number of the standard disease area ranges is unique, then retrieve the first standard disease type corresponding to the standard disease area range from a preset disease type database; The first standard damage type is set as the actual damage type of the bridge.

6. The method according to claim 5, characterized in that After retrieving the standard disease range corresponding to the actual disease area from the preset disease type database, the method further includes: If the number of the quasi-disease ranges is not unique, a second standard disease type corresponding to each standard disease area range is retrieved from a preset disease type database; Searching a preset disease category database for standard disease images corresponding to the second standard disease category; intercepting a picture of an actual damaged area in the actual bridge picture; Performing a similarity comparison between the actual diseased area image and the standard diseased area image to obtain an actual similarity value; Determine whether the actual similarity value is within a preset similarity standard range; if so, retrieve a second standard disease category corresponding to the standard disease image from a preset disease category database; The second standard damage type is set as the actual damage type of the bridge.

7. The method according to claim 6, characterized in that After determining whether the actual similarity value is within a preset similarity standard range, the method further includes: If the actual similarity value is outside the preset similarity standard range, a prompt message related to the disease type confirmation with the actual disease area picture is sent to the background management platform; Receiving the third standard disease type sent back by the backend management platform; The third standard damage type is set as the actual damage type of the bridge.

8. A bridge disease monitoring system based on drones, characterized by include: An image acquisition unit (201) is used to acquire, in real time, actual bridge surface image information of the bridge monitoring area sent by the image acquisition device according to a preset image acquisition path; An orientation analysis unit (202) is used to analyze the actual orientation of the bridge to determine if the actual bridge surface image information is located at the actual orientation of the bridge, thereby obtaining a bridge image at the actual orientation; A picture query unit (203) is used to query an initial bridge picture corresponding to the actual position in a preset standard bridge picture database; A region identification unit (204) is used to determine whether the actual bridge image and the standard bridge image are consistent; if not, to identify inconsistent regions between the actual bridge image and the standard bridge image; A defect determination unit (205) is used to mark inconsistent areas on the actual bridge image and set the inconsistent areas as defect areas; The damage display unit (206) is used to send the actual bridge picture with the damaged area marked to the background management platform for display.

9. An intelligent terminal, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and which executes the method according to any one of claims 1 to 7.

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