嵌入双注意力机制的Faster R-CNN航拍输电线路螺栓缺陷检测  

Bolt defect detection for aerial transmission lines using Faster R-CNN with an embedded dual attention mechanism

作　　者：戚银城[1,2] 武学良 赵振兵[1,2] 史博强 聂礼强 Qi Yincheng;Wu Xueliang;Zhao Zhenbing;Shi Boqiang;Nie Liqiang(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding 071003,China;School of Computer Science and Technology,Shandong University,Qingdao 266237,China)

机构地区：[1]华北电力大学电气与电子工程学院,保定071003 [2]华北电力大学河北省电力物联网技术重点实验室,保定071003 [3]山东大学计算机科学与技术学院,青岛266237

出　　处：《中国图象图形学报》2021年第11期2594-2604,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61871182,61773160);北京市自然科学基金项目(4192055);河北省自然科学基金项目(F2020502009);中央高校基本科研业务费专项资金资助(2018MS095,2020YJ006);模式识别国家重点实验室开放课题基金项目(201900051)。

摘　　要：目的螺栓是输电线路上最普遍、数量最多的部件,螺栓缺陷检测是输电线路视觉检测任务的一大难点。针对螺栓目标存在背景复杂、目标过小、不同类别之间差异小以及精细特征难以提取的问题,提出一种双注意力机制方法,分别对不同尺度和不同位置的视觉特征进行分析和增强。方法对于不同尺度的特征,网络使用不同尺度的特征图计算出相应的注意力图,然后将相邻层的注意力图差异性作为正则化项加入网络中,从而增强螺栓区域的精细特征。对于不同位置的特征,先使用特征图计算出图像的空间注意力图,注意力图中每个元素表示两个空间位置的相似程度,然后利用注意力图将局部特征与全局特征融合,从而在全局视野上增强相似的区域,达到增大螺栓与背景的特征差异程度,实现提高螺栓区域的预测效果。结果本文在航拍输电线路典型螺栓数据集上进行测试,与基线相比,结合双注意力机制的航拍输电线路螺栓检测方法的平均准确率提高了2.21%,其中正常螺栓类提升了0.29%,缺销螺栓类提升了5.23%,螺母缺失螺栓类提升了1.1%。结论本文提出的基于双注意力机制的航拍输电线路螺栓缺陷检测方法取得了良好的效果,有效避免了螺栓缺陷检测中的误判漏判问题,为进一步对输电线路其他缺陷任务奠定了良好的基础。Objective In transmission lines,bolts are widely used as a kind of fasteners to connect various parts of transmission lines and make the overall structure stable and safe.However,bolts are easily damaged because of their complex working environment.The damage or loss of a bolt may cause a large area of transmission line failure,which seriously threatens the safety and stability of the power grid.Bolts are the most common components of transmission lines.Thus,bolt defect detection is an important task in transmission line inspection.Good features are difficult extract because of the complex background,small target,small difference between categories,and loss of gradient information.This study proposes a dualattention scheme to enhance the visual features of different scales and positions.Method First,for different scales,the network extracts the feature map of each layer,uses the multi-scale attention model to obtain the corresponding attention map,calculates the difference of the attention map for adjacent layers,and adds it to the loss function as a regularization term to enhance the fine features of the bolt area.The trained network continuously reduces the difference in the attention maps of different layers.The learned attention maps of different scales are introduced into the network as a kind of context information.This procedure can avoid the loss of important information in the process of feature extraction.No additional regulatory information is required because the attention map is from the network itself.Second,for different positions,bolts appear in specific positions of the accessories,but due to light blocking and other reasons,the characteristics of these positions are not obvious.In this study,we use the feature map to derive a spatial attention map of the image.Each element in the attention map indicates the degree of similarity between two spatial locations.Then,the attention map is used to combine the features of each position with the global feature.This process enhances the features in simila

关 键 词：双注意力机制 多尺度 空间位置 螺栓缺陷检测 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TM75[自动化与计算机技术—计算机科学与技术]