引入Self-Attention的电力作业违规穿戴智能检测技术研究

Research on Intelligent Detection Technology for Illegal Wearing in Power Operation by Introducing Self-Attention

随着电网建设的高速发展,作业现场技术支撑人员规模不断扩大。电力现场属于高危作业场所,违规穿戴安全防护用品将会严重危及作业人员的人身安全,为了改善传统人工监管方式效率低下的问题,本文采用实时深度学习算法进行违规穿戴行为检测。检测模型结合实时目标检测网络YOLOv3和Self-Attention机制,借鉴DANet结构,在YOLOv3网络高层嵌入自注意力模块,更好地挖掘和学习特征位置和通道关系。实验结果表明,该模型在违规穿戴检测任务中mAP达到了94.58%,Recall达到了96.67%,与YOLOv3相比,mAP提高了12.66%,Recall提高了2.69%,显著提高模型的精度,可以满足任务的检测需求,提升了电网智能化水平。

With the rapid development of power grid construction,the scale of technical support personnel in operation site is expanding continuously.Operation site belongs to high-risk work site,illegal wearing protective equipment will seriously endanger the workers.In order to improve the inefficiency of traditional manual supervision,this paper uses a real-time deep learning algorithm to detect illegal wearing behavior.The algorithm combines the real-time object detection network YOLOv3 and Self-Attention mechanism,uses the DANet structure for reference,and embeds the Self-Attention module at the high level layers of YOLOv3 network to better mine and learn location relations and channel relations of feature maps.The experimental results show that the mAP and Recall of this algorithm reached 94.58%and 96.67%.Compared with YOLOv3,its mAP and Recall increased by 12.66%and 2.69%.The accuracy of the model is significantly improved,which can meet the detection requirements of the task and improve the intelligence of power grid.