Research on Highway Fire Detection Based on Smoke Motion Characteristics
The current fire detection methods still have the problems of low detection accuracy and high computational resource consumption.This paper designs a highway fire detection method based on the motion of smoke combined with deep network.The moving foreground in the video is extracted by Gaussian mixture background modeling,and the region of interest is select-ed for the moving foreground to obtain the potential smoke area,and the HSV color space analysis is performed on the area to determine the presence of smoke.For video frames with smoke,a highway-specific fire and smoke dataset with more than 50000 object annotation boxes is constructed,combined with the YOLOv5 detection method,to achieve a mean average precision(mAP)of 90.16％.The proposed method avoids frame-by-frame fire detection,greatly reduces the waste of computing re-sources,and has research and practical engineering application values.
smoke detectionfire and smoke datasettraffic video analysisdeep learninghighway fire
万方数据
维普
