Road disaster remote sensing intelligent detection method based on deep learning

A technology of intelligent detection and deep learning, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problems of road disaster detection accuracy dependence, road extraction accuracy, long detection time, and low degree of automation, etc., to achieve narrow detection Range, noise removal detection, effect of narrowing the range
CN112990100APending Publication Date: 2021-06-18AEROSPACE INFORMATION RES INST CAS
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
AEROSPACE INFORMATION RES INST CAS
Publication Date
2021-06-18

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Abstract

The invention discloses a deep learning-based road disaster remote sensing intelligent detection method. The method comprises the following steps of A, image preprocessing: performing radiometric calibration, atmospheric correction, geometric correction, ortho-rectification and image registration on a remote sensing image; B, establishing a road buffer area: according to the road vector data and the image resolution, establishing an area 3-4 times based on the road width as the road buffer area; C, image masking: performing masking processing on the remote sensing image according to the established road buffer area; D, image block cutting: cutting the masked remote sensing image into a plurality of image blocks; E, image block prediction: inputting the cut image blocks, namely effective image blocks, into the trained YOLOV3 network, and predicting the probability that the image blocks belong to different damage categories of the road; and F, merging the image blocks: merging the predicted image blocks to generate an image result image with the original size. The method is convenient and easy to operate, and an accurate road disaster detection distribution result is obtained.
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Description

technical field

[0001] The invention belongs to the technical field of remote sensing image processing, and more specifically relates to an intelligent detection method for road disasters based on deep learning, which is suitable for optical remote sensing images with a resolution of 0.5m-2m. Background technique

[0002] Transportation plays an indispensable and important role in post-earthquake rescue. However, due to the impact of the earthquake, transportation elements are often damaged to varying degrees and cannot pass normally, which brings many challenges to the entire rescue operation. Rescue has the biggest impact. Therefore, how to quickly and accurately obtain the road disaster situation after the earthquake is very important for the smooth progress of the entire post-disaster rescue activities. Relying on traditional methods, it is difficult to quickly understand the disaster situation of the roads in the disaster-stricken area from the ground, so that it is im...

Claims

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