基于MBDC和双重注意力的变电站人员穿戴检测

Wear detection of substation staff based on MBDC and dual attention

安全帽与工作服是变电站工作人员安全的重要保障,为解决现有检测模型对其检测精度低的问题,本文提出了MBDC和双重注意力的变电站人员穿戴检测算法。该算法提出了多分支深度卷积(multi branch deep convolution,MBDC)网络增加深度可分离卷积层以增强特征提取的完备性;然后提出多通道交互注意力(multimodal interaction attention,MIA)增加模型对小目标的检测能力,并将MIA机制结合高效通道注意力(efficient channel attention,ECA)机制构成双重注意力机制,增强模型对于小目标和遮挡目标的识别精度;最后引入焦点损失函数和SIOU(scylla intersection over union)作为损失函数以解决正负样本不平衡问题并加快收敛速度。实验表明,本文算法全类平均精度达到84.88%,比原算法高9.92%,总体性能优于对比算法。

Helmets and work clothes are important guarantees for the safety of substation staff.In order to solve the problem of low detection accuracy of existing detection models,this paper proposes a substation staff wear detection algorithm based on multi-branch deep convolution and dual attention.The algorithm proposes a multi-branch deep convolution(MBDC)network to add a deep separable convolution layer to enhance the completeness of feature extraction.Then,it is proposed that multimodal interaction attention(MIA)can increase the detection ability of the model to small targets,and combine the MIA mechanism with efficient channel attention(ECA)mechanism to form a dual attention mechanism to enhance the recognition accuracy of the model for small targets and occluded targets.Finally,the focus loss function and SIOU(scylla intersection over union)are introduced as the loss function to solve the problem of positive and negative sample imbalance and accelerate the convergence speed.The experiment shows that the average accuracy of the algorithm in this paper is 84.88%,9.92%higher than the original algorithm,and the overall performance is better than the comparison algorithm.