【摘要】 针对当前火灾检测效果差和抗干扰能力弱等问题，提出一种增强感受野特征的多尺度火灾检测方法.首先，引入感受野注意力卷积(receptive-field attention convolution, RFAConv),增强感受野空间特征的提取；其次，结合反向残差移动模块(inverted residual mobile block, iRMB)和通道先验卷积注意力(channel prior convolutional attention, CPCA)设计C2fiC模块，提高模型表达和融合不同尺度特征的能力；然后，采用共享参数结构，引入轻量卷积重构检测头，降低模型参数和计算复杂度；最后，引入Focaler-GIoU损失函数，平衡难易样本.实验结果表明，改进模型参数量和计算量均有所降低，检测精度更高，能满足火灾场景中的检测要求.更多还原

【Abstract】 Aiming at the problems of poor fire detection effect and weak anti-interference ability, a multi-scale fire detection method based on enhanced receptive field feature is proposed. Firstly, the Receptive-Field Attention Convolution(RFAConv)is introduced to enhance the extraction of spatial features of receptive field. Secondly, the C2fiC module is designed by combining the Inverted Residual Mobile Block(iRMB)and the Channel Prior Convolutional Attention(CPCA)mechanism to improve the ability of the model to express and fuse different scale features. Then, the shared parameter structure is adopted, and the lightweight convolution reconstruction detector is introduced to reduce the model parameters and computational complexity. Finally, the Focaler-GIoU loss function is introduced to balance the difficulty samples. The experimental results show that the number of parameters and the amount of calculation of the improved model are reduced, and the detection accuracy is higher, which can meet the detection requirements in flame detection.更多还原