Unmanned aerial vehicle forest flame recognition method based on deep learning

Abstract

The invention relates to an unmanned aerial vehicle forest flame recognition method based on deep learning, and belongs to the technical field of unmanned aerial vehicle vision and digital image processing. The method comprises the following steps: firstly, preprocessing a returned flame image by using histogram equalization and multistage median filtering algorithms, then detecting suspected flame pixels of a video by using a color space model algorithm, and then performing opening operation processing on the video image by using mathematical morphology. According to the invention, the improved median filtering is used to remove the noise of the video image; compared with common median filtering, the improved filtering not only reserves the special effect of the filtering in a phase analysis processing method of an optical measurement image, but also adopts a fringe center analysis method, the spatial density of impulse noise is relatively reduced by expanding a window, so that the details of the image are better, and meanwhile, the processing efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicle vision and digital image processing, and specifically relates to an unmanned aerial vehicle forest flame recognition method based on deep learning. Background technique [0002] With the rapid development of science and technology, unmanned operation has become the current development trend, and the development prospects are broad. UAVs often perform some special tasks, which undoubtedly proposes better recognition capabilities and flexibility for UAVs. higher requirement. The UAV forest fire prevention method based on video flame recognition is characterized by simple structure, low cost, and high cost performance. The processing accuracy is higher, which is more conducive to the accurate identification of drones. [0003] In the commonly used framing method, the number of frames of the video must first be determined. It takes a long time to load all frame images and consumes a...

AI Technical Summary

Problems solved by technology

The median filtering method is very effective in eliminating salt and pepper noise, and it has a special effect in the phase analysis processing method of optically measured fringe images, but it has little effect in the fringe center analysis method

[0007] Therefore, at present, the traditional median filter is not ideal in terms of noise reduction processing and detail protection, which makes the identification result of the drone inaccurate, thus affecting the recognition of the flame by the drone

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