An Ultra-Wideband Microwave Diagnostic Method for Detecting Cracks in Polymeric Insulators

Yin, Jungang; Wu, Runqi; Yang, Jian; Xu, Xiangdong; Fan, Chong

doi:10.1109/tap.2020.3008654

Cited by 4 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several traditional detection methods for insulator defects. Yin et al [8] proposed an innovative ultra-wideband microwave fault diagnostic system using the multimode transfer theory of dielectric waveguides. This system detects internal defects by leveraging the level difference of mode transfer.…”

Section: Introductionmentioning

confidence: 99%

Insulator Defect Detection Based on YOLOv8s-SwinT

He,

Yang,

Liu

et al. 2024

Information

View full text Add to dashboard Cite

Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method leveraging YOLOv8s-SwinT. Combining Swin Transformer and Convolutional Neural Network (CNN) enhances the model’s understanding of multi-scale global semantic information through cross-layer interactions. The improved BiFPN structure in the neck achieves bidirectional cross-scale connections and weighted feature fusion during feature extraction. Additionally, a new small-target detection layer enhances the capability to detect tiny defects. The experimental results showcase outstanding performance, with precision, recall, and mAP reaching 95.6%, 95.3%, and 97.7%, respectively. This boosts detection efficiency and ensures high accuracy, providing robust support for real-time detection of tiny insulator defects.

show abstract

Section: Introductionmentioning

confidence: 99%