Mountain fire detection method and system based on static and dynamic multi-feature fusion

Abstract

The invention discloses a mountain fire detection method and system based on static and dynamic multi-feature fusion. The method comprises the following steps: firstly, performing frame sampling on aforest fire video to generate an initial image sequence, and preprocessing the initial image sequence to generate a preprocessed image sequence; dividing the preprocessed image sequence into a staticsample and a dynamic sample, and extracting static features of the static sample and dynamic features of the dynamic sample by using a convolutional neural network; fusing the static features and thedynamic features by using a kernel canonical correlation analysis method to generate fused features; inputting the fused features into a support vector machine classifier for learning and training, and generating a trained forest fire detection classifier; and carrying out mountain fire detection by adopting the trained mountain fire detection classifier. According to the method, in the process ofextracting the image features based on the video frames, the static features and the dynamic features of the preprocessed images are extracted respectively and then fused, the forest fire features can be extracted comprehensively, and the precision and the accuracy of forest fire detection are improved.

Description

technical field [0001] The invention relates to the technical field of mountain fire detection, in particular to a method and system for detecting mountain fires with static and dynamic multi-feature fusion. Background technique [0002] With the rapid development of the power grid and the continuous expansion of the grid structure, it is inevitable that the power grid must pass through mountains and forests, so wildfires have become a situation that must be prevented when laying the power grid. Because we are in the mountains, the terrain is complex and the area is vast, manual monitoring is difficult, and the automatic monitoring of wildfires can greatly reduce the waste of manpower. The rapid development of image processing technology and computer vision provides a technical basis for automatic computer detection of wildfires. [0003] Most of the current wildfire recognition and detection methods use convolutional neural networks alone or use traditional feature extract...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a static and dynamic multi-feature fusion mountain fire detection method and system to solve the problems th

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