基于CXANet⁃YOLO的火焰检测方法

It is great significant for reducing fire hazards based fast and accurate flame detection. In order to strengthen the flame feature extraction capability of the model and solve the problem of feature map size imbalance，CXANet⁃block (Convolution Extremely Attention Network) is built as the backbone network of YOLOv5 with XSepConv (Extremely Separated Convolution)，large convolution kernel，Mish activation function，etc. CBAM (Convolution Block Attention Module) is introduced，and a CXANet⁃YOLO⁃based flame detection method is proposed to improve the detection performance by increasing channel attention and spatial attention. It is trained on the self⁃built flame dataset to improve the robustness and generalization of the model. Experimental results show that the CXANet⁃YOLO model has higher detection accuracy and detection speed than the benchmark model YOLOv5 in flame detection. The accuracy rate is increased by 8.2%，and the detection speed is added by 25 frames per second.

Keywords： deep learning ; flame detection ; attentional mechanism ; YOLOv5