计算机与数字工程2025，Vol.53Issue(4)：1155-1163,9.DOI:10.3969/j.issn.1672-9722.2025.04.041
雾霾天气下输电线路典型金具缺陷检测识别
Identification of Typical Metal Defects in Transmission Lines Under Hazy Weather Based on Convolutional Attention
摘要
Abstract
In view of the negative impact of hazy weather on the imaging quality of transmission line aerial images can reduce the accuracy of the typical metallic tool defect detection and recognition algorithm for transmission lines,this paper proposes a me-tallic tool defect recognition algorithm for transmission lines based on an improved AOD-Net network model and the ReVGG recogni-tion algorithm.The improved AOD-Net network model introduces a feature fusion module and uses the human perceptual vision MS-SSIM loss function to improve the defogging effect,repair image colour distortion and improve image quality.Defect recognition is based on the ReVGG recognition algorithm,and the lightweight SimAM attention module is embedded to enhance the feature ex-traction capability of the network.Experimental results show that the improved AOD-Net algorithm has 4.25%higher information en-tropy than the original algorithm,which improves the fog removal effect,the recognition effect improves the accuracy by 2.1%com-pared with the ReVGG algorithm.The overall algorithm uses a model with small space occupation,good real-time performance and facilitates edge deployment.关键词
雾霾天气/AOD-Net/去雾/ReVGG/无参注意力模块Key words
hazy weather/AOD-Net/de-misting/ReVGG/non-referential attention module分类
信息技术与安全科学引用本文复制引用
李飞,王紫仪,陈瑞,孟琳,陈烨,邹辉军..雾霾天气下输电线路典型金具缺陷检测识别[J].计算机与数字工程,2025,53(4):1155-1163,9.基金项目
国家自然科学基金青年基金项目(编号:61903183) （编号:61903183）
江苏省未来网络科研基金项目(编号:FNSRFP2021YB26) （编号:FNSRFP2021YB26）
江苏省高等学校基础科学(自然科学)面上项目(编号:21KJB120005)资助. （自然科学）
