Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

doi:10.1007/s11633-022-1355-y

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Transmission Line Insulator Defect Detection Based on Swin Transformer and Context
	Yu Xi 4
刊名	Machine Intelligence Research
	2023
卷号	20 期号:5 页码:729-740
关键词	Insulator defect, object detection, Swin transformer, data augmentation, context information
ISSN号	2731-538X
DOI	10.1007/s11633-022-1355-y
英文摘要	Insulators are important components of power transmission lines. Once a failure occurs, it may cause a large-scale blackout and other hidden dangers. Due to the large image size and complex background, detecting small defect objects is a challenge. We make improvements based on the two-stage network Faster R-convolutional neural networks (CNN). First, we use a hierarchical Swin Trans former with shifted windows as the feature extraction network, instead of ResNet, to extract more discriminative features, and then design the deformable receptive field block to encode global and local context information, which is utilized to capture key clues for de tecting objects in complex backgrounds. Finally, the filling data augmentation method is proposed for the problem of insufficient defects and more images of insulator defects under different backgrounds are added to the training set to improve the robustness of the model. As a result, the recall increases from 89.5% to 92.1%, and the average precision increases from 81.0% to 87.1%. To further prove the superiority of the proposed algorithm, we also tested the model on the public dataset Pascal visual object classes (VOC), which also yields outstanding results.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/52448]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China 2.Institute of Electric Power Research of Guizhou Power Grid, Guiyang 550000, China 3.Institute of Electric Power Research of Guangxi Power Grid, Nanning 530023, China 4.Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510000, China
推荐引用方式 GB/T 7714	Yu Xi. Transmission Line Insulator Defect Detection Based on Swin Transformer and Context[J]. Machine Intelligence Research,2023,20(5):729-740.
APA	Yu Xi.(2023).Transmission Line Insulator Defect Detection Based on Swin Transformer and Context.Machine Intelligence Research,20(5),729-740.
MLA	Yu Xi."Transmission Line Insulator Defect Detection Based on Swin Transformer and Context".Machine Intelligence Research 20.5(2023):729-740.