Forest fire disaster detection method based on videos

Abstract

The invention provides a forest fire disaster detection method based on videos. Based on unique growth change features of forest fire disaster smoke, by judging growth change conditions in a candidate smoke region, forest fire disaster smoke detection is achieved, so it is not easy for objects in other colors and similar shapes to interfere with the smoke detection; effects imposed by other types of moving objects can be effectively eliminated; the method is quite high in detection robustness; in the provided detection method based on early videos of the forest fire disasters, accumulation regions are applied and comparison is performed for many times in a certain frame number interval based on the accumulation regions, so judgment and analysis for growth change of the regions are quite stable; and when a forest fire disaster happens early, through detection of the smoke, problems of automatic detection and alarming of the fire disaster based on a remote-distance monitoring video are effectively solved.

Description

technical field [0001] The invention belongs to the fields of forest fire prevention and video target detection, and in particular relates to a video-based forest fire detection method. Background technique [0002] Traditional fire detection and alarm technology usually utilizes sensors capable of sensing smoke particles. However, these sensors only work when they are close to fireworks, and the working distance is very limited. In addition, due to the fast air flow in open field conditions, it is difficult for smoke particles to be effectively received by the sensors. The video-based fire detection method uses the deployed remote monitoring cameras to monitor whether a fire occurs within a relatively long distance around. For large-scale forest or mountain forest areas, it is generally necessary to set up watchtowers at a certain distance and deploy monitoring cameras to cover the entire area, and transmit all real-time acquired videos to the monitoring center. It takes ...

AI Technical Summary

Problems solved by technology

In addition, the method of segmenting potential smoke areas based on motion features may also fail for long-distance smoke, because long-distance smoke generally moves slowly in the video, and it is difficult to effectively and accurately obtain smoke areas through motion information

In addition, machine learning and training techniques are also widely used in video detection of fire smoke. Such techniques require large-scale training samples, but in practice, people can obtain very limited forest fire video data of various scenes; for Long-distance video smog generally lacks significant features such as texture and shape for machine learning technology to learn and train due to unclear imaging and small smoke areas, which also makes the final detection effect unsatisfactory.

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