Improved Flame Target Detection Algorithm Based on YOLOv3

This paper proposes an improved flame target detection algorithm based on YOLOv3 (You only look once, v3) to solve the causes of reduced detection accuracy caused by fire on small and medium-sized target, multi target, and fuzzy edge. First, the improved feature pyramid network makes use of local information twice. Then, a large-scale full convolution module is designed to obtain global spatial information of various scales, and an improved channel space attention mechanism is used to improve effective information and suppress useless information. Finally, as loss functions, complete intersection-over-union and Focal Loss are used to improve the detection accuracy of difficult-to-recognise targets and alleviate the problem of data set imbalance. Experimental results in self-made flame data show that this algorithm has higher detection accuracy and faster detection speed. The average accuracy is up to 89.82%, and the detection speed can reach 20. 2FPS (Frames per second), enabling it to meet real-time and high-efficiency fire detection requirements.