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September 10, 2025SensorsOpen Access

YOLOv5s-TC: An Improved Intelligent Model for Insulator Fault Detection Based on YOLOv5s

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Authors

YYYingying YinJilin Agricultural University YDYunpeng DuanYunnan University XWXin WangUniversity of Illinois Chicago

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Implication

This study demonstrates improved detection accuracy in insulator fault detection using YOLOv5s-TC, highlighting advancements in UAV technology and deep learning.

Key Points

YOLOv5s-TC model achieves a 4.4% improvement in mean average precision over YOLOv5s, enhancing insulator fault detection.
Incorporating Bottleneck Transformers and CBAM, YOLOv5s-TC focuses on key image features to boost localization ability.
The revised OSIoU loss function in YOLOv5s-TC further increases detection accuracy for power grid inspections.
Comparative experiments show that YOLOv5s-TC outperforms Faster R-CNN and SSD with significant performance gains in precision.

Cite This Study

Yin et al. (2025) studied this question.

synapsesocial.com/papers/68c1c62654b1d3bfb60f1a58 https://doi.org/10.3390/s25164893

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Implication

This study demonstrates improved detection accuracy in insulator fault detection using YOLOv5s-TC, highlighting advancements in UAV technology and deep learning.

YOLOv5s-TC: An Improved Intelligent Model for Insulator Fault Detection Based on YOLOv5s

Discussion

Member takes

Implication

Key Points

Cite This Study

YOLOv5s-TC: An Improved Intelligent Model for Insulator Fault Detection Based on YOLOv5s

Discussion

Member takes

Implication

Key Points

Cite This Study

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