Fire Detection based on Convolutional Neural Networks with Channel Attention

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.