电力信息与通信技术2025，Vol.23Issue(4)：25-34,10.DOI:10.16543/j.2095-641x.electric.power.ict.2025.04.04
基于YOLOv7的均压环缺陷及倾斜角度检测方法
Detection Method for Defects and Tilt Angle of Grading Ring Based on YOLOv7
摘要
Abstract
Aiming at the problem that it is difficult to detect the defects and tilt angle of the grading ring of transmission lines,an improved YOLOv7 grading ring defect and rotated object detection model is proposed.The backbone network of YOLOv7 is designed as a two-branch network,which combines the local features of CNN and the global representation of Transformer to enhance the expression ability of object features.A progressive feature fusion structure is used to make the model more effective in fusing shallow and deep features.A Gaussian modeling representation of rotated box detection is introduced in the detection head to improve the accuracy of angle detection.Finally,based on the improved rotated object detection model,a grading ring tilt angle detection algorithm is designed to realize the detection of the grading ring tilt angle under any viewing angle.The experimental results show that the average angular error of the improved algorithm is reduced by 0.97° to only 5.32°,and the mAP increased by 4.1%to 91.5%.The algorithm significantly improves the mean average precision in the defect detection of grading ring,and provides an effective solution for the quantitative detection of grading ring defects and tilt angle.关键词
均压环/深度学习/倾斜角度检测/缺陷检测/特征融合Key words
grading ring/deep learning/tilt angle detection/defect detection/feature fusion分类
动力与电气工程引用本文复制引用
黄泽泽,董晓杰,李少龄,付建文,康泰安,戚银城..基于YOLOv7的均压环缺陷及倾斜角度检测方法[J].电力信息与通信技术,2025,23(4):25-34,10.基金项目
河北省省级科技计划资助(SZX2020034). （SZX2020034）
