A Weak Fire Detection Method Based on YOLOv5 and PCA

For the version 5 of you only look once (YOLOv5) based fire detection method, the confidence scores of weak fire regions are usually low, which can affect the fire detection rate. For the above problem, a novel weak fire detection method based on the YOLOv5 network and principal component analysis (YOLOv5-PCA) is proposed. Firstly, the YOLOv5 network is used to process real-time monitoring images and produce potential fire regions with confidence scores. If the confidence score of a potential region is medium, the principal component analysis is further applied to fuse the pixel color features of the potential region. A pixel monitoring statistic is built to detect weak fire pixels. In a potential region, if the proportion of fire pixels exceeds 1/3, it is considered that there is fire, and a fire alarm is activated. Simulation studies on 5049 images from 25 fire videos and 424 images from 2 non-fire videos are used to verify the effectiveness of the proposed method. Compared with the YOLOv5 based method, the proposed YOLOv5-PCA method can effectively increase fire detection rate by 49.06%, and correct many misidentified non-fire regions.