重庆工商大学学报（自然科学版）2026，Vol.43Issue(3)：19-29,11.DOI:10.16055/j.issn.1672-058X.2026.0003.003
N-YOLOv8:绝缘子缺陷自动检测模型
N-YOLOv8:An Automatic Detection Model for Insulator Defects
摘要
Abstract
Objective The normal operation of insulators is a crucial part of ensuring the safe and reliable operation of power systems.To promote the automatic detection of insulator defects,a lightweight detection model,N-YOLOv8,was designed.Methods For network lightweighting,a lightweight module called DPHConv was first designed by integrating depthwise separable convolution,BN layer,Hardswish activation function,and residual connections.Second,a lightweight module,CFI-X,was designed by integrating Inception-Bottleneck and C2f.These two modules significantly reduced the number of network parameters at the cost of only a minor loss in detection accuracy.In terms of improving detection accuracy,a T-ECA module was designed based on the ECA attention mechanism.Then,an attention mechanism PEG was designed by integrating the T-ECA module with a multi-branch parallel structure and a Gather Excite attention module.Furthermore,CEIoU_Loss was designed by combining the ideas of CIoU_Loss and EIoU_Loss.Finally,Soft-NMS was introduced to replace the NMS in the original network.These improvements effectively boosted the network's performance in detecting insulator defects.Results Compared with the YOLOv8n network,N-YOLOv8 reduced the number of parameters by 43％and floating-point operations by 37％,while achieving a detection accuracy of 91.7％,which was 0.2％higher than that of the original network.Conclusion N-YOLOv8 delivers high detection performance,which can effectively facilitate the deployment of intelligent detection algorithms on UAV platforms and achieve real-time detection of insulator defects.关键词
缺陷检测/智能检测/深度可分离卷积/PEG注意力机制/CEIoU_LossKey words
defect detection/intelligent detection/depth-separable convolution/PEG attention mechanism/CEIoU_Loss分类
信息技术与安全科学引用本文复制引用
宋鸿绅,贾晓芬..N-YOLOv8:绝缘子缺陷自动检测模型[J].重庆工商大学学报（自然科学版）,2026,43(3):19-29,11.基金项目
国家自然科学基金面上项目资助(52174141) （52174141）
安徽省自然科学基金面上项目资助(2108085ME158) （2108085ME158）
安徽理工大学引进人才科研启动基金资助(2022YJRC44). （2022YJRC44）
