A Small-Sized Defect Detection Method for Overhead Transmission Lines Based on Convolutional Neural Networks

Fu, Qi; Liu, Jiefeng; Zhang, Xingtuo; Zhang, Yiyi; Ou, Yang; Jiao, Runsheng; Li, Chuanyang; Mazzanti, G.

doi:10.1109/tim.2023.3298424

Cited by 10 publications

(1 citation statement)

References 25 publications

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“…Fu et al [26] proposed the I2D-Net which incorporates innovative modules to enhance defect localization in the presence of interference factors to address the challenge of detecting small-sized insulator defects. Li et al [27] significantly boosted the small-target detection capabilities of the YOLOv7 network by leveraging spatial pyramid pooling and the innovative cross-stage partial channel (SPPCSPC) module, along with integrating the CA module and dynamic convolution module.…”

Section: Related Workmentioning

confidence: 99%

Insulator Defect Detection Based on YOLOv8s-SwinT

He,

Yang,

Liu

et al. 2024

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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.

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Section: Related Workmentioning

confidence: 99%