面向输电线路绝缘子的GER-YOLO缺陷检测算法
GER-YOLO Fault-Detection Algorithm for Transmission-Line Insulators
袁博雅 1李尧 2叶青1
作者信息
- 1. 长江大学计算机科学学院,湖北 荆州 434023
- 2. 长江大学电子信息与电气工程学院,湖北 荆州 434023
- 折叠
摘要
针对无人机检测绝缘子缺陷存在的算法参数量大、图像背景复杂、绝缘子尺度变化大等问题,提出了一种新的用于绝缘子缺陷检测的GER-YOLO算法.首先,利用GhostNetV2构建C2fGhostV2模块,能够在显著减少参数量和计算量的同时维持算法检测精度.然后,引入具有跨空间学习能力的高效多尺度注意力(EMA)网络,充分挖掘特征信息,抑制无用信息.最后,提出C2fRFE模块,进一步捕获长程信息,学习多尺度特征,提高对不同尺度绝缘子及其缺陷的检测能力.实验结果表明,GER-YOLO算法相较于基线模型YOLOv8s,平均精度均值(mAP)提升了1.1%,参数量、计算量分别减少了30.2%和31.0%,该算法能够有效完成绝缘子缺陷检测任务.
Abstract
A novel algorithm named GER-YOLO for insulator defect detection is proposed to address the issues of large algorithm parameters,complex image backgrounds,and significant insulator-scale changes in the unmanned-aerial-vehicle detection of insulator defects.First,GhostNetV2 is used to construct the C2fGhostV2 module,which significantly reduces the number of parameters and computation while maintaining the algorithm's detection accuracy.Second,an efficient multi-scale attention(EMA)network with cross-spatial-learning ability is introduced,which enables the complete mining of feature information and suppresses meaningless information.Finally,the C2fRFE module is proposed to capture long-range information,learn multiscale features,and improve the detection ability of insulators and their defects at different scales.Experimental results show that compared with the baseline model YOLOv8s,GER-YOLO offers a higher mean average precision(mAP)by 1.1%,reduces the parameter and computational costs by 30.2%and 31.0%,respectively,and can effectively detect insulator defects.
关键词
绝缘子缺陷检测/轻量化/注意力机制/多尺度信息Key words
insulator defect detection/lightweight/attention mechanism/multiscale information引用本文复制引用
出版年
2024
NETL
NSTL
万方数据