Video smoke detection methods combining improved YUV color model and image blocks

Abstract

At present, most unmanned aerial vehicles (UAV) smoke detection systems transmit video back to the ground station computer for analysis to determine whether a fire has occurred, Since the image transmission process takes a certain amount of time and interferes with various interference sources, the response time of smoke detection and the calculation amount of subsequent image processing are increased. In order to reduce the response time of smoke detection, this paper proposes a smoke detection method suitable for UAVs to achieve smoke detection at the UAVs. The improved YUV color model is used to filter and block the video images acquired by the UAVs. Extract the spatiotemporal and dynamic features of smoke; These smoke features are trained and classified using a support vector machine (SVM) to detect the presence of smoke in the video image. Experimental results show that compared with the commonly used smoke detection methods, the accuracy of smoke detection is significantly improved, and the response time is greatly reduced.