摘要
为了维持电力传输的可靠性、安全性和可持续性,航拍玻璃绝缘子的自爆缺陷诊断成为电力巡检中一项重要的任务。为此,提出了一种轻量化的玻璃绝缘子自爆缺陷检测模型L-YOLOv5。首先,对主干网络中的残差模块进行轻量化改进,添加深度可分离卷积和1×1组卷积,设计主干网络L-CSPDarknet53,此网络可以有效提高模型检测速度。在特征提取方面,设计了DC-SPP模块,模块中卷积与空洞卷积串联的方式可以在不损失细节信息的情况下增大感受野,提高网络的检测性能。最后,针对自爆缺陷区域小难以检测的问题,提出增加小目标检测层的方法,小目标检测层包含更多缺陷细节信息,更加有利于自爆缺陷的检测。实现结果表明,L-YOLOv5可以快速准确的检测自爆缺陷,其中准确率可达到96.7%,检测速度达到37.4帧/s,相比于YOLOv5网络,准确率和速度分别提升了3.5%和49%。与Faster-RCNN、SSD等常用检测网络相比,L-YOLOv5在绝缘子缺陷识别和定位问题上具有更强的竞争力。
In order to maintain the reliability,safety and sustainability of power transmission,the fault diagnosis of aerial insulators has become an important task in power inspection.Therefore,a lightweight defect detection model L-YOLOv5 is proposed in this paper.First,the residual module in the backbone network is improved for light weight by adding depthwise separable convolution and 1×1 group convolution,and by designing the backbone network L-CSPDarknet53.This network can greatly improve the detection speed without sacrificing a small amount of accuracy.In terms of feature extraction,the DC-SPP module is designed.The convolution and dilated convolution in the module can increase the receptive field and improve the detection performance of the network without losing detailed information.Finally,in accordance with the problem that the self explosion defect area is small and difficult to detect,a method of adding a small target detection layer is proposed.The small target detection layer contains more defect details,which is more conducive to the detection of self explosion defects.The implementation results show that L-YOLOv5 can quickly and accurately detect self explosion defects,with an accuracy of 96.7%and a detection speed of 37.4 frames/s.Compared with YOLOv5 network,the accuracy and speed are improved by 3.5%and 49%,respectively.Compared with other common detection networks such as Faster R-CNN and SSD,L-YOLOv5 is more competitive in insulator defect identification and location.
作者
王道累
张世恒
袁斌霞
赵文彬
朱瑞
WANG Daolei;ZHANG Shiheng;YUAN Binxia;ZHAO Wenbin;ZHU Rui(College of Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200240,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2023年第10期4382-4390,共9页
High Voltage Engineering
基金
国家自然科学基金(12172210,61502297)。
关键词
轻量化
玻璃绝缘子
自爆缺陷
深度可分离卷积
空洞卷积
lightweight
glass insulator
self exploding defects
depthwise separable convolution
dilated convolution
