Abstract: Abstract: To solve the problems of low accuracy and slow speed in manual and sensor fire detection methods, based on the analysis of fire features, this paper designs an improved YOLOv5 multiplex fire detection algorithm incorporating adjustable coordinate residual attention. The YOLOv5 network adopted in our algorithm can automatically extract and learn the features of the images. First, the location information is embedded into the channel attention through the coordinate attention mechanism, which enables the network to obtain a larger range of information and improves the detection accuracy of fire images. Secondly, the initial features of the flame are retained through residual connections, and the initial features are adaptively combined with the coordinate attention features to achieve better performance. Finally, the detection and recognition with high accuracy are performed on real?time video streams captured by multiple cameras. The method proposed in this paper can identify and detect not only the flame information generated by fire, but also the smoke generated in the early stage of fire to reduce the loss of missing the best remediation time in the early stage of fire.

Key words: fire detection, attention mechanism, object detection, deep learning