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A regional risk factor assessment method using UAV oblique photographic images

A technology of oblique photography and risk factor, which is applied in unmanned aircraft, image data processing, details involving 3D image data, etc., can solve the problems of risk factor evaluation in difficult-to-monitor areas, unobvious disaster characteristics, and increased detection difficulty , to achieve the effect of reducing complex background interference, reducing the missed detection rate and high accuracy

Active Publication Date: 2022-08-02
CHINA HIGHWAY ENG CONSULTING GRP CO LTD +1
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Problems solved by technology

This traditional method has the following defects: when the geological disaster is located on a slope with a large slope, the area in the orthophoto image is small, the disaster features are not obvious, and it is easy to cause missed detection; when the background of the geological disaster area is complex, it will lead to Increased difficulty of detection
And after the traditional detection, only the area of ​​geological hazards can be obtained, and it is difficult to evaluate the overall risk coefficient of the monitoring area

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  • A regional risk factor assessment method using UAV oblique photographic images
  • A regional risk factor assessment method using UAV oblique photographic images
  • A regional risk factor assessment method using UAV oblique photographic images

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[0018] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0019] like figure 1 As shown, the present invention utilizes the method for evaluating the regional risk factor of the oblique photographic image of the UAV, including the following steps:

[0020] S101: Perform the same-name point matching, forward intersection and beam method adjustment for the overlapping area of ​​the UAV oblique photographic image and the pose of each image obtained by the UAV POS system, so as to obtain accurate external parameters (line elements and corner elements).

[0021] S102: For the original image data of the oblique photographic image in step 1, a multi-scale fully convolutional neural network considering context information is used to segment the image to accurately extract the pixel coordinates of the geological hazard contour. The above neural network has added a weighted attention mechanism module for detecting geologica...

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Abstract

The invention discloses a regional risk coefficient evaluation method using the oblique photographic images of unmanned aerial vehicles. From oblique photographic images, a deep learning image segmentation method is used to detect geological disaster areas, and based on the precise external parameters obtained by the adjustment of the unmanned aerial vehicle image beam method. The real geographical location of the disaster area is calculated, and the risk coefficient of the monitoring area is further analyzed in combination with the area and slope data of the detected disaster. Compared with the traditional method, the invention uses the original photo data to detect the landslide, so the detection rate of the landslide is higher, and the risk coefficient of the detection area can be output.

Description

technical field [0001] The invention belongs to the field of geological exploration, and relates to a regional risk factor evaluation method using oblique photographic images of unmanned aerial vehicles. Background technique [0002] At present, the traditional method of interpreting geological hazards from UAV images is to obtain DOM and DSM by intensive matching of images, and then perform stitching and digital correction to obtain orthophotos. Then, for the orthophoto, the geological hazard area in the orthophoto is detected by using technical means such as dependent feature design and machine learning, and then the regional safety assessment is completed. This traditional method has the following shortcomings: when the geological disaster is located on a slope with a large slope, the area on the orthophoto image is small, the disaster characteristics are not obvious, and it is easy to cause missed detection; when the geological disaster area background is complex, it wil...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C11/00G01C11/04G06N3/04G06T17/20B64C39/02
CPCG01C11/00G01C11/04G06T17/20B64C39/02G06T2200/04G06N3/045B64U2101/30
Inventor 张蕴灵侯芸傅宇浩杨璇龚婷婷陈志杰孙雨崔丽宋张亮郭沛张鹏
Owner CHINA HIGHWAY ENG CONSULTING GRP CO LTD
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