Research on fire detection algorithm based on deep learning

Abstract

Aiming at the problems of low accuracy and long detection time of traditional fire detection algorithms, a fire detection method DAI-YOLO based on improved YOLOv3 is proposed. On the basis of the YOLOv3 algorithm, the network structure is improved, the scale of the input image is increased, the traditional convolution is replaced with a depthwise separable convolution structure, the model parameters are reduced, and the detection rate is improved; the multi-scale feature detection is used to increase the shallow detection scale and add 4 times upsampling feature fusion structure to improve fire detection accuracy; optimize k-means clustering algorithm. The experimental results show that the accuracy and recall rate of the improved algorithm are 91.2% and 84.2%, the mAP is as high as 84.6%, and the detection speed can reach 0.31 s, which can meet the real-time and accuracy of fire detection.