自监督E-Swin的输电线路金具检测  

Self-supervised E-Swin based transmission line fittings detection

作　　者：张珂[1,2] 周睿恒 石超君 韩槊 杜明坤 赵振兵[1,2] Zhang Ke;Zhou Ruiheng;Shi Chaojun;Han Shuo;Du Mingkun;Zhao Zhenbing(Department of Electronic and Communication 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)

机构地区：[1]华北电力大学电子与通信工程系,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

出　　处：《中国图象图形学报》2023年第10期3064-3076,共13页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62076093,62206095,61871182);中央高校基本科研业务费专项资金资助(2023JG002,2022MS078,2023JC006)。

摘　　要：目的输电线路金具种类繁多、用处多样,与导线和杆塔安全密切相关。评估金具运行状态并实现故障诊断,需对输电线路金具目标进行精确定位和识别,然而随着无人机巡检采集的数据逐渐增多,将全部数据进行人工标注愈发困难。针对无标注数据无法有效利用的问题,提出一种基于自监督E-Swin Transformer(efficient shifted win⁃dows Transformer)的输电线路金具检测模型,充分利用无标注数据提高检测精度。方法首先,为了减少自注意力的计算量、提高模型计算效率,对Swin Transformer自注意力计算进行优化,提出一种高效的主干网络E-Swin。然后,为了利用无标注金具数据加强特征提取效果,针对E-Swin设计轻量化的自监督方法,并进行预训练。最后,为了提高检测定位精度,采用一种添加额外分支的检测头,并结合预训练之后的主干网络构建检测模型,利用少量有标注的数据进行微调训练,得到最终检测结果。结果实验结果表明,在输电线路金具数据集上,本文模型的各目标平均检测精确度(AP50)为88.6%,相比传统检测模型提高了10%左右。结论本文改进主干网络的自注意力计算,并采用自监督学习,使模型高效提取特征,实现无标注数据的有效利用,构建的金具检测模型为解决输电线路金具检测的数据利用问题提供了新思路。Objective Transmission line is a key of infrastructure of power system.To keep the stability of the power sys⁃tem,it is required to preserve key components-based operation in the transmission line like fittings.Fittings are recognized as aluminum or iron-made metal accessories for multiple applications in relevant to such domains of protective fittings,con⁃necting fittings,tension clamps and suspension clamps.Fittings can be mainly used to support,fix and connect bare con⁃ductors and insulators.Such components are erosional for such complicated natural environment year by year.They are likely to have displacement,deflection and damage,which will affect the stability of the transmission system structure.If the defects of fittings are not sorted out quickly,they will cause severe circuit-damaged accidents.To assess status of the fittings and realize fault diagnosis,it is required to locate and identify the target of the transmission line fittings accurately.The emerging deep learning and unmanned aerial vehicle inspection techniques have been developing to optimize conven⁃tional single manual inspection technology further.A maintenance mode is melted into gradually,which can use unmanned aerial vehicle to acquire images,and the deep learning method is then incorporated to process aerial photos automatically.Most of these methods are focused on supervised learning only,that is,model training-before artificial data annotation is required for.As more and more data on transmission line components are collected by unmanned aerial vehicle patrols,manual labeling requires a large amount of human resources,and such missing and incorrect labeling problems will be occurred after that.To resolve this problem,we develop a fitting detection model based on self-supervised Transformer.Self-supervised learning is focused on unlabeled data-related pretext task design to mine the feature representation of the data itself and improve the feature extraction ability of the model.Less supervised data is then used for fine-tuning

关 键 词：深度学习 目标检测 输电线路金具 自监督学习 E-Swin Transformer模型 一阶段检测器 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]