华南理工大学学报（自然科学版）2026，Vol.54Issue(2)：25-37,13.DOI:10.12141/j.issn.1000-565X.250172
基于改进YOLOv5s的输电塔螺栓松动检测
Transmission Tower Bolt Looseness Detection Based on Improved YOLOv5s
摘要
Abstract
As the critical infrastructure in power transmission networks,the structural stability of transmission towers directly impacts the safe and reliable operation of the power grid.During long-term service,bolts in the tower structure are prone to gradual loosening under the coupled effects of multiple factors such as wind loads,temperature variations,and material aging.This paper proposed an intelligent detection model for bolt looseness in transmission towers based on an improved YOLOv5s(named CCSGS-YOLO).The model incorporates several key enhancements:coordinate convolution replaces standard convolutional layers in the backbone network to strengthen the model's ability to capture positional information of targets;a convolutional block attention module(CBAM)is introduced to strengthen the model's feature discrimination capability in complex backgrounds through dual channel and spatial attention mechanisms;a slim-neck feature fusion architecture is constructed,leveraging an optimized combination of cross-stage partial connections and depthwise separable convolutions to reduce computational complexity while maintaining detection accuracy;a joint optimization strategy employing the Generalized Intersection over Union(GIoU)loss function and Soft Non-Maximum Suppression(Soft-NMS)improves localization accuracy by considering the geometric overlap characteristics between predicted and ground-truth bounding boxes.Experimental results demonstrate that CCSGS-YOLO achieves a precision of 91.7%,a recall of 89.4%,a mean average precision(mAP)of 95.3%,and an F1 score of 90.0%.These metrics represent improvements of 1.6,3.0,1.4,and 1.0 percentage points,respectively,over the baseline YOLOv5s model.In terms of computational efficiency,the model achieves a detection speed of 74.8 frames per second(FPS),reducing the inference latency to 13.4 ms,which represents an 11.6%improvement compared to the YOLOv5s model.Furthermore,this paper validates the detection robustness of CCSGS-YOLO across various scenarios through field experiments,providing a novel approach for intelligent inspection of loose bolts on transmission towers.关键词
输电塔/螺栓松动/深度学习/目标检测Key words
transmission tower/bolt looseness/deep learning/target detection分类
信息技术与安全科学引用本文复制引用
王德弘,张子轩..基于改进YOLOv5s的输电塔螺栓松动检测[J].华南理工大学学报（自然科学版）,2026,54(2):25-37,13.基金项目
吉林省科技发展计划项目中青年科技创新人才(团队)培育项目(20250601091RC) （团队）
吉林省"长白山英才计划"项目(202441208)Supported by the Youth Science and Technology Innovation Talent(Team)Cultivation Project of Jilin Province Science and Technology Development Plan(20250601091RC)and Jilin Province"Changbai Mountain Talent Program"Project(202441208) （202441208）
