南方电网技术2024，Vol.18Issue(11)：159-168,10.DOI:10.13648/j.cnki.issn1674-0629.2024.11.017
基于X-DR图像与YOLO-MS模型的输电线路耐张线夹压接缺陷检测
Crimping Defect Detection of Transmission Line Strain Clamp Based on X-DR Image and YOLO-MS Model
摘要
Abstract
Aiming at the massive X-DR images generated by crimping quality detection of transmission line strain clamps,an intel-ligent recognition method for crimping defects is proposed based on YOLO-MS model.A X-DR image dataset including 6 typical crimping defects is constructed using the field crimping quality detection images of strain clamps,and the image preprocessing is car-ried out by Gaussian filtering,histogram equalization,and gamma correction.The multi-scale(MS)object detection network YOLO-MS is constructed using CSPDarknet,CBAM-PANet,and Head-4.The model is trained and tested using concentrated training and testing samples from a dataset.The results show that the YOLO-MS model can effectively detect 6 types of strain clamp crimping defects,with a mean average precision of 92.57%,and a detection speed of 26 frames per second.It can be used to assist transmis-sion line operation and maintenance personnel to carry out automatic recognition and defect detection of strain clamp crimping images.关键词
输电线路/耐张线夹/X射线图像/压接质量/缺陷检测Key words
transmission line/strain clamp/X-ray image/crimping quality/defect detection分类
信息技术与安全科学引用本文复制引用
李俊轩,邱志斌,石大寨,张润,李攀..基于X-DR图像与YOLO-MS模型的输电线路耐张线夹压接缺陷检测[J].南方电网技术,2024,18(11):159-168,10.基金项目
国家自然科学基金资助项目(52167001) （52167001）
江西省"双千计划"创新领军人才长期(青年)项目(jxsq2019101071). Supported by the National Natural Science Foundation of China(52167001) （青年）
the Innovative Leading Talents Long-Term Project of Jiangxi"Double Thousand Plan"(jxsq2019101071). （jxsq2019101071）
