Fire detection method based on lightweight YOLOv4

A fire detection model with improved YOLOv4 is proposed for mobile devices with limited computing resources and less accurate object localization. Firstly, the network model of YOLOv4 object detection algorithm is modified, and a depthwise separable convolution network is used instead of traditional convolution in the feature extraction network part to realize the lightweight of fire detection model. Then the Loss function is optimized to solve the problem of inaccurate object detection frame localization. The experimental results show that compared with YOLOv4, the improved algorithm reduces the model parameter by 60.7 % and the detection speed increases by 27.9 %. It is more favorable for the model to be equipped on mobile devices.