广东电力2024，Vol.37Issue(5)：104-111,8.DOI:10.3969/j.issn.1007-290X.2024.05.011
基于稀疏重构注意力机制的绝缘子缺陷检测方法
Insulator Defect Detection Based on Sparse Reconstruction Dual Attention Mechanism
摘要
Abstract
Aiming at the current problem of feature redundancy and low detection accuracy in the detection process caused by the small number of defective samples and complex background of transmission line insulators,this paper proposes a target detection model based on the sparse reconstruction dual attention(SRDA)mechanism.Firstly,to mitigate the influence of redundant deep features,it employs a sparse reconstruction mechanism to filter the deep feature layer of the model.Secondly,to enrich the model's capability in delineating target regions across various contexts,the paper introduces a positional attention mechanism capturing contextual cues from the shallow feature target region.Thirdly,by integrating a channel attention mechanism to augment the feature representation of specific semantic categories within the deep feature layer,the semantic portrayal of defective targets is enhanced.Finally,the research conducts defect detection experiments using UAV-captured images of insulators on the transmission lines.The results demonstrate the model's efficacy in discerning accurate defect features,thus improving the detection accuracy of insulator defects,surpassing performance benchmarks set by other models.关键词
稀疏重构/绝缘子缺陷检测/注意力机制/语义信息Key words
sparse reconstruction/insulator defect detection/attention mechanism/semantic information分类
信息技术与安全科学引用本文复制引用
刘敏,周国亮,王红旭,郑怿..基于稀疏重构注意力机制的绝缘子缺陷检测方法[J].广东电力,2024,37(5):104-111,8.基金项目
国网冀北电力有限公司科技项目(520184220001) （520184220001）
