基于区域注意力机制和多尺度特征融合的输电线路螺栓缺陷检测

Defect Detection of Transmission Line Bolt Based on Region Attention Mechanism and Multi-scale Feature Fusion

螺栓在输电线路中起到了固定线路间连接的作用,一旦出现松动或者脱落,就可能会导致电力传输发生故障而引起大范围停电事故。显然,定时对输电线上的螺栓进行检测,对确保整个电力系统的安全稳定有着至关重要的作用。现有的检测方法大多基于深度卷积神经网络,然而螺栓特征不明显、尺寸小的特点给检测工作带来了挑战。针对上述问题,提出了一种基于区域注意力机制和多尺度特征融合的输电线路螺栓缺陷检测方法。首先,提出了适用于目标检测的区域注意模块,将该模块嵌入至ResNet50的残差块中以增强网络对螺栓的特征提取。其次,在特征金字塔结构(FPN)的基础上,扩展一条自下而上的路径,同时对浅层特征进行充分利用,以提高对小物体的检测精度。最后,为了缓解样本间的不平衡问题,引入了PrIme Sample Attention(PISA)软样本采样策略。实验结果表明,所提方法在检测输电线螺栓时,均值平均精度(mAP)达到了74.3%,平均召回率(AR)达到了86.4%,检测速度为8.2FPS。与其他检测网络相比,所提方法在不牺牲太多检测速度的基础上,提高了对螺栓缺陷的检测精度。

Bolts play a role in fixing the connection between lines in transmission lines.Once loose or detached,it may cause po-wer transmission failures and cause large-scale power outages.Obviously,regular inspection of bolts in transmission lines is essential to ensure the safety and stability of the entire power system.Most of the existing detection methods are based on deep convolutional neural networks.However,the unobvious features and small size of the bolts pose a challenge to the detection work.Aiming at the above problems,this paper proposes a bolt defect detection method for transmission lines based on region attention mechanism and multi-scale feature fusion.Firstly,a region attention module suitable for object detection is proposed,which is embedded in the residual block of ResNet50 to enhance the network’s feature extraction for bolts.Secondly,based on the feature pyramid networks(FPN),a bottom-up path is extended,and shallow features are fully utilized to improve the detection accuracy of small objects.Finally,in order to alleviate the imbalance between samples,the PrIme Sample Attention(PISA)soft sample sampling strategy is introduced.Experimental results show that the proposed method achieves a mean average precision(mAP)of 74.3%and an average recall(AR)of 86.4%with a detection speed of 8.2 FPS when detecting transmission line bolts.Compared with other detection networks,the proposed method improves the detection accuracy of bolt defects without sacrificing too much detection speed.