CN115562284A - Method for realizing automatic inspection by high-speed rail box girder inspection robot - Google Patents
Method for realizing automatic inspection by high-speed rail box girder inspection robot Download PDFInfo
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Abstract
The invention discloses a method for realizing automatic inspection by a high-speed rail box beam inspection robot, which comprises the following steps: collecting surrounding environment data and absolute position information of the robot, and generating an environment map; combining the absolute position information of the robot with an environment map, and obtaining a patrol route of the robot based on a route planning algorithm; controlling the robot to move, and carrying out global shooting on the interior of the high-speed rail beam in real time to obtain image information of the high-speed rail beam; constructing an initial HED edge detection model, and training the initial HED edge detection model to obtain a target HED edge detection model; and inputting the image information of the high-speed railway box girder acquired in real time into a target HED edge detection model for processing to obtain a target defect image of the high-speed railway box girder, thereby realizing the defect detection of the high-speed railway box girder. The method for realizing automatic inspection of the high-speed railway box girder has high detection speed and high efficiency, and can prevent workers from detecting flaws in high-risk working environments.
Description
Technical Field
The invention belongs to the field of inspection of defects of high-speed railway box girders, and particularly relates to a method for realizing automatic inspection of a high-speed railway box girder inspection robot.
Background
In the economic development of China, high-speed rails occupy a more important position, and the quality of the high-speed rails determines the operation safety and the road smoothness of the high-speed rails. The box girder is used as a key component of a high-speed rail bridge and bears train load transmitted by a high-speed rail. Under the combined action of various adverse factors, such as traffic load, gradual degradation of building materials, influence of different environments and the like, certain damage can be caused to the integral structure and the internal structure of the high-speed rail box girder, fatigue cracking can occur under extreme conditions, and the operation safety of the high-speed rail is seriously threatened.
Domestic detection to the high-speed railway case roof beam mostly takes manual inspection as the main thing, and detection efficiency is low, detection precision is poor, leak hunting rate height etc. and the staff detects a flaw under high-risk, harsh, cruel operational environment for a long time, also does not do benefit to staff's physical and mental health. The existing automatic inspection method for the box girder of colleges and universities cannot adapt to the complex environment in the high-speed rail box girder, the detection precision is low, and the utilization rate is low. Therefore, the rapid automatic detection of the high-speed rail box girder is realized, and the support is provided for the subsequent maintenance guarantee.
Disclosure of Invention
The invention aims to provide a method for realizing automatic inspection of a high-speed rail box girder inspection robot, which aims to solve the problems in the prior art.
In order to achieve the aim, the invention provides a method for realizing automatic inspection by a high-speed rail box girder inspection robot, which comprises the following steps:
acquiring surrounding environment data and absolute position information of the robot, and generating an environment map based on the surrounding environment data;
combining the absolute position information of the robot with an environment map, and obtaining a patrol route of the robot based on a route planning algorithm;
controlling the robot to move based on the routing inspection path of the robot, and carrying out global shooting on the interior of the high-speed railway box girder in real time to obtain image information of the high-speed railway box girder;
constructing an initial HED edge detection model, and training the initial HED edge detection model based on the image information of the high-speed railway box girder to obtain a target HED edge detection model;
and inputting the image information of the high-speed railway box girder acquired in real time into the target HED edge detection model for processing to obtain a target defect image of the high-speed railway box girder, thereby realizing the defect detection of the high-speed railway box girder.
Optionally, the inspection robot is provided with an NB-IOT sensor, a panoramic camera, a laser radar and a synchronous controller; the panoramic camera includes a stereo image sensor; the lidar includes a laser scanner and a laser scanning sensor.
Optionally, the process of collecting the ambient data and the absolute position information of the robot includes,
the NB-IOT sensor is connected with an NB-IOT base station set in the surrounding environment to acquire the position information of the inspection robot; the panoramic camera and the laser radar scan and measure the periphery of the inspection robot to obtain relative three-dimensional point cloud data of the surrounding environment; and registering the position information and the relative three-dimensional point cloud data to obtain absolute three-dimensional point cloud data, and further obtain absolute position information of the robot.
Optionally, before the routing inspection path of the robot is obtained based on the path planning algorithm, acquiring relative attitude information of the robot in the surrounding environment based on an attitude sensor, combining the relative attitude information of the robot with absolute position information, and planning the routing inspection path of the robot in an environment map based on the path planning algorithm; wherein the attitude sensor is mounted on the robot.
Optionally, carry on the industry on the robot and detect the camera and carry out global shooting to the high-speed railway case roof beam, the industry detects the camera and includes top surface detection camera, left side detection camera, right side detection camera and bottom surface detection camera.
Optionally, the training process of the initial HED edge detection model includes delineating the boundary of each target object in the high-speed railway box girder image, and storing the boundary in a vector data format; rasterizing the vector data, converting the rasterized vector data into an edge image, manufacturing an edge training sample of the high-speed railway box girder image and the edge image based on a preset manufacturing size, and training the initial HED edge detection model based on the edge training sample.
Optionally, the process of obtaining the internal defect image of the high-speed rail box girder includes segmenting the image of the high-speed rail box girder based on a preset ratio to obtain a plurality of image blocks; inputting the image blocks into a target HED edge detection model for detection and splicing to obtain a complete edge probability graph; carrying out binarization, skeleton extraction and vector derivation processing on the complete edge probability map to obtain a vector polygon; simplifying the vector polygon to obtain an image segmentation result of the high-speed railway box girder, and further obtaining a target defect image of the high-speed railway box girder;
the simplification processing comprises eliminating sawtooth on the boundary of the vector polygon and finely crushing the vector polygon holes.
Optionally, the process of detecting the defects of the high-speed rail box girder includes obtaining relative position information of a target defect image of the high-speed rail box girder based on position information of the industrial detection camera carried on the robot; acquiring absolute position information of the robot in real time based on the NB-IOT sensor; and acquiring the position information of the hidden danger points in the high-speed railway box girder based on the relative position information of the target defect image and the absolute position information of the robot, and finishing the inspection of the high-speed railway box girder.
The invention has the technical effects that:
the method for realizing automatic inspection of the high-speed railway box girder not only can realize automatic detection of the defects of the high-speed railway box girder, but also has high detection speed and high efficiency, and can prevent workers from detecting flaws in high-risk, severe and harsh working environments;
according to the method, the position information of the hidden danger points in the high-speed rail box girder can be obtained according to the relative position information of the target defect image of the high-speed rail box girder and the absolute position information of the inspection robot, so that the later maintenance and overhaul work is facilitated, the work efficiency of overhaul and maintenance is improved, and timely maintenance and powerful support can be provided for the safe operation of a high-speed rail;
the HED edge detection model adopted by the invention does not need to set any parameter when in use, thereby greatly simplifying the difficulty of use; and the segmentation effect of the model is better, the segmentation scale is finer, the boundary is more accurate, and the accuracy of detecting the defect point of the high-speed railway box girder is greatly improved.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of an automatic inspection method according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
Example one
As shown in fig. 1, the present embodiment provides a method for realizing automatic inspection by a high-speed railway box girder inspection robot, including the following steps:
acquiring surrounding environment data and absolute position information of the robot, and generating an environment map based on the surrounding environment data; combining the absolute position information of the robot with an environment map, and obtaining a patrol route of the robot based on a route planning algorithm; controlling the robot to move based on the routing inspection path of the robot, and carrying out global shooting on the interior of the high-speed railway box girder in real time to obtain image information of the high-speed railway box girder; constructing an initial HED edge detection model, and training the initial HED edge detection model based on the image information of the high-speed railway box girder to obtain a target HED edge detection model; and inputting the image information of the high-speed railway box girder acquired in real time into a target HED edge detection model for processing to obtain a target defect image of the high-speed railway box girder, thereby realizing the defect detection of the high-speed railway box girder.
The inspection robot is provided with an NB-IOT sensor, a panoramic camera, a laser radar and a synchronous controller; the panoramic camera includes a stereo image sensor; the lidar includes a laser scanner and a laser scanning sensor.
The practical process of collecting the ambient environment data and the absolute position information of the robot comprises the steps that the NB-IOT sensor is connected with an NB-IOT base station set in the ambient environment, and the position information of the inspection robot is obtained; the panoramic camera and the laser radar scan and measure the periphery of the inspection robot to obtain relative three-dimensional point cloud data of the surrounding environment; and registering the position information and the relative three-dimensional point cloud data to obtain absolute three-dimensional point cloud data and further obtain absolute position information of the robot.
The NB-IOT base station in this embodiment can receive the GNSS signal and obtain the absolute spatial coordinates of the NB-IOT base station. The NB-IOT sensor can establish data exchange with the NB-IOT base station by utilizing the characteristics of wide signal coverage area and strong penetration capability of the narrowband Internet of things. The panoramic camera and the laser radar in the embodiment are both used for acquiring three-dimensional space point cloud data of the surrounding environment of the inspection robot.
In this embodiment, the NB-IOT base station includes at least three movable NB-IOT sensing devices and GNSS receivers connected thereto, which are distributed around the periphery of the environment to be measured.
The synchronous controller is adopted in the embodiment, and the purpose of the synchronous controller is to enable the measured position information and the collected three-dimensional point cloud data to have a uniform time reference, so that the accuracy of the finally obtained point cloud data can be improved. And when the position information of the inspection robot and all the point cloud data are acquired, uploading the acquired position information and all the point cloud data to a computer, and then registering the position information of the inspection robot and the relative three-dimensional point cloud data by using a point cloud data registration method to further eliminate errors, thereby improving the accuracy of the finally obtained absolute three-dimensional point cloud data.
The method comprises the following steps that before the routing inspection path of the robot is obtained based on a path planning algorithm, the relative attitude information of the robot in the surrounding environment is collected based on an attitude sensor, the relative attitude information of the robot is combined with absolute position information, and the routing inspection path of the robot is planned in an environment map based on the path planning algorithm; wherein the attitude sensor is mounted on the robot.
The utility model has the advantages of can carry out, patrol and examine the robot and examine time measuring according to the route of patrolling and examining of planning, rely on the industry that carries on the robot to detect the camera and carry out the global shooting to the high-speed railway box girder, the industry detects the camera and includes top surface detection camera, left surface detection camera, right flank detection camera and bottom surface detection camera.
The robot is capable of detecting obstacles by carrying an infrared sensor and/or an ultrasonic sensor; when the obstacle is detected to exist on the final walking path, the robot stops walking and judges whether the existence time of the obstacle reaches a preset threshold value or not; if the existence time of the obstacle does not reach a preset threshold value, determining that the obstacle is a temporary obstacle, and continuing to walk according to a final walking path after the obstacle disappears; and if the existence time of the obstacle reaches a preset threshold value, determining that the obstacle is a fixed obstacle, and after the robot bypasses the obstacle, returning to the final walking path and continuing to walk according to the final walking path. The determination of the preset threshold value can be selected according to actual needs.
The method comprises the following steps that after the global image of the high-speed railway box girder is obtained based on an industrial detection camera, the image of the high-speed railway box girder is analyzed and processed based on an HED edge detection model, and then a target defect image is obtained.
Specifically, an initial HED edge detection model is constructed, and the initial HED edge detection model is trained, wherein the training process comprises the steps of drawing the boundary of each target object in the high-speed railway box girder image and storing the boundary in a vector data format; and rasterizing the vector data, then converting the vector data into an edge image, making an edge training sample on the basis of a preset making size for the high-speed rail box girder image and the edge image, training the initial HED edge detection model on the basis of the edge training sample, and obtaining a target HED edge detection model.
Dividing the image of the high-speed rail box girder based on a preset proportion to obtain a plurality of image blocks; inputting a plurality of image blocks into a target HED edge detection model for detection and splicing to obtain a complete edge probability graph; carrying out binarization, skeleton extraction and vector derivation processing on the complete edge probability map to obtain a vector polygon; simplifying the vector polygon to obtain an image segmentation result of the high-speed rail box girder so as to obtain a target defect image of the high-speed rail box girder;
the simplified processing comprises eliminating sawtooth on the boundary of the vector polygon and finely crushing the holes of the vector polygon, so that the vector polygon has better visual effect. Eliminating the broken polygons by adopting an area threshold method, setting a threshold value, and deleting the polygons with the areas smaller than the threshold value. The elimination of holes should first read the geometric points of each polygon of the vector, only keep the outermost ring geometric points, and discard the inner geometric points.
Further, based on the position information carried on the robot by the industrial detection camera, obtaining the relative position information of the target defect image of the high-speed rail box girder; acquiring absolute position information of the robot in real time based on the NB-IOT sensor; and acquiring the position information of the hidden danger points in the high-speed railway box girder based on the relative position information of the target defect image and the absolute position information of the robot, and finishing the inspection of the high-speed railway box girder.
After the HED edge detection model adopted in the embodiment is trained, no parameter needs to be set during use, and individual parameters required in the processing process of model calculation result data can be automatically calculated through image characteristics. When the method is used, only the image to be segmented and the output segmentation vector path need to be specified, so that the use difficulty is greatly simplified. Compared with an object-oriented and depth-oriented segmentation model, the segmentation effect of the embodiment is better, the segmentation scale is finer, and the boundary is more accurate. And the trained model can run under the GPU, the parallel computing efficiency of the model is obviously higher than that of the CPU, and compared with an object-oriented image segmentation algorithm only using the CPU, the image segmentation efficiency can be greatly improved.
The method for realizing automatic inspection of the high-speed rail box girder not only can realize automatic detection of the defects of the high-speed rail box girder, but also has high detection speed and high efficiency, can enable workers to avoid flaw detection in high-risk, harsh and harsh working environments, avoids subjectivity of inspection results, and can provide timely maintenance and powerful support for safe operation of high-speed rails.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method for realizing automatic inspection by a high-speed rail box girder inspection robot is characterized by comprising the following steps:
collecting surrounding environment data and absolute position information of a robot, and generating an environment map based on the surrounding environment data;
combining the absolute position information of the robot with an environment map, and obtaining a patrol route of the robot based on a route planning algorithm;
controlling the robot to move based on the routing inspection path of the robot, and carrying out global shooting on the interior of the high-speed railway box girder in real time to obtain image information of the high-speed railway box girder;
constructing an initial HED edge detection model, and training the initial HED edge detection model based on the image information of the high-speed railway box girder to obtain a target HED edge detection model;
and inputting the image information of the high-speed rail box girder acquired in real time into the target HED edge detection model for processing to obtain a target defect image of the high-speed rail box girder, thereby realizing the defect detection of the high-speed rail box girder.
2. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 1,
the inspection robot is provided with an NB-IOT sensor, a panoramic camera, a laser radar and a synchronous controller; the panoramic camera includes a stereoscopic image sensor; the lidar includes a laser scanner and a laser scanning sensor.
3. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 2, is characterized in that,
the process of collecting the ambient data and the absolute position information of the robot includes,
the NB-IOT sensor is connected with an NB-IOT base station set in the surrounding environment to acquire the position information of the inspection robot; the panoramic camera and the laser radar scan and measure the periphery of the inspection robot to obtain relative three-dimensional point cloud data of the surrounding environment; and registering the position information and the relative three-dimensional point cloud data to obtain absolute three-dimensional point cloud data, and further obtain absolute position information of the robot.
4. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 1, is characterized in that,
before obtaining the inspection path of the robot based on a path planning algorithm, acquiring relative attitude information of the robot in the surrounding environment based on an attitude sensor, combining the relative attitude information of the robot with absolute position information, and planning the inspection path of the robot in an environment map based on the path planning algorithm; wherein the attitude sensor is mounted on the robot.
5. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 1,
carry on industry detection camera on the robot and carry out the global shooting to the high-speed railway case roof beam, industry detection camera includes top surface detection camera, left surface detection camera, right flank detection camera and bottom surface detection camera.
6. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 1,
the training process of the initial HED edge detection model comprises the steps of drawing the boundary of each target object in the high-speed railway box girder image and storing the boundary in a vector data format; rasterizing the vector data, converting the rasterized vector data into an edge image, manufacturing an edge training sample of the high-speed railway box girder image and the edge image based on a preset manufacturing size, and training the initial HED edge detection model based on the edge training sample.
7. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 1,
the process of obtaining the internal defect image of the high-speed rail box girder comprises the steps of dividing the image of the high-speed rail box girder based on a preset proportion to obtain a plurality of image blocks; inputting the image blocks into a target HED edge detection model for detection and splicing to obtain a complete edge probability graph; carrying out binarization, skeleton extraction and vector derivation processing on the complete edge probability map to obtain a vector polygon; simplifying the vector polygon to obtain an image segmentation result of the high-speed railway box girder, and further obtain a target defect image of the high-speed railway box girder;
the simplification processing comprises eliminating sawtooth on the boundary of the vector polygon and finely crushing the vector polygon holes.
8. The method for realizing automatic inspection by the inspection robot for the high-speed railway box girder according to claim 1, is characterized in that,
the method comprises the steps that the relative position information of a target defect image of the high-speed railway box girder is obtained based on the position information of an industrial detection camera carried on a robot; acquiring absolute position information of the robot in real time based on the NB-IOT sensor; and acquiring the position information of the hidden danger points in the high-speed rail box girder based on the relative position information of the target defect image and the absolute position information of the robot, and finishing the inspection of the high-speed rail box girder.
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| CN118210315A (en) * | 2024-05-21 | 2024-06-18 | 中交公路长大桥建设国家工程研究中心有限公司 | Robot-based steel box girder inspection method and system |
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