Fire detection method based on YOLOv5 model

For fire detection in People's Daily life can effectively avoid property losses and casualties, based on this, an improved smoke fire identification algorithm based on YOLOv5s model is proposed. The feature extraction module of YOLOv5s was redesigned, and the CA attention module was introduced into the C3 module to build a new feature extraction module C3CA, which enhanced the feature extraction capability. The neck conv module is improved to AKConv module, which reduces the parameters required in the running of the model, speeds up the reasoning speed and improves the timeliness of the model. For the feature fusion part of the network, BAFPN, which combines the repeated bidirectional cross-scale connection with the weighted feature fusion mechanism on the basis of BiFPN and the AVCStem modules, can better focus on the features of small and medium-sized targets in different situations and complex backgrounds. It effectively solves the problem of difficult to determine the edge range and fuzzy target shape for detecting such objects as flame and smoke. The experimental results show that the improved map index is increased by 6.9%.