【摘要】 快速准确的火焰检测对于降低火灾危害具有重要意义，为了加强模型的火焰特征提取能力以及解决特征图尺寸不平衡的问题，利用XSepConv (Extremely Separated Convolution)、大卷积核、Mish激活函数等构建CXANet-block(ConvolutionExtremelyAttentionNetwork)作为YOLOv5的骨干网络，引入CBAM (ConvolutionBlockAttention Module)注意力机制，提出一种基于CXANet-YOLO的火焰检测方法，通过增加通道注意力和空间注意力来提高检测性能.在自建火焰数据集上进行训练，提升模型的鲁棒性和泛化能力.实验结果表明，CXANet-YOLO模型比基准模型YOLOv5在火焰检测上具有更高的检测精度和检测速度，准确率提高了8.2%，检测速度每秒提升25帧.更多还原

【Abstract】 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.更多还原