Analysis Of Forest Fire Detection Algorithm Based On Video Fusion Technology

Forest is one of the most important resources on earth,and the forest fire is a significant factor threatening forest health.So how to detect the forest fire accurately and timely is a big subject for the scholars at home and abroad.The traditional method of detecting the forest fire is based on hardware equipment monitoring and artificial patrolling.It means low coverage and consuming a large amount of manpower and material resources.And the traditional method is greatly influenced by forest environment.Firstly,the article studied the forest fire detection based on the single band video of visible light and infrared.The algorithm applied the background subtraction method.At the same time,the algorithm used color,luminance and dynamic features of fire images to detect the forest fire.The experiment results show that the proposed algorithms based on single band data can find the region of forest fire in the video with some missing and false detection.In order to improve the accuracy,the forest fire detection algorithm based on infrared and visible light fusion was mainly researched in this thesis.A new forest fire detection algorithm based on fractional-calculus theory was proposed,in which the fused video was achieved from the infrared video and the visible light video using the fractional-calculus operator.In the fused video,the background subtractionInethod was firstly used to determine if there is an abnormal phenomenon occurred.This abnormal case is an indication of the forest fire possibility.Then,a threshold segmentation method was applied to the current frame and the difference image between the current frame and background frame if the fused video is abnormal.Finally,the forest fire region will be detected with these two segmented inages.Experimental results demonstrate that the proposed algorithm has the best fusion performance,and also has the better performance on accuracy rate and real-tine.The proposed algorithm is efficient and accurate and it provides a favorable new approach for the forest fire detection.