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<p>This paper proposes an improved YOLOv5-based method for automatic detection of broken strand defects in distribution network conductors. To address challenges in complex environments and slender target localization, we introduce a lightweight neck network, channel attention mechanism, and an efficient feature extraction network (EFEN). Experiments on a self-built UAV inspection dataset of 1,364 images show that the improved model achieves 0.89 precision and 0.88 mAP, outperforming the original YOLOv5 by 6%. Field tests on over 1&#xa0;km of power lines demonstrate an 88.4% detection rate with no false alarms.</p>

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Automatic detection of broken strands defects in distribution network conductors based on deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Mingxin Zuo,
  • Haoxiang Hu,
  • Keke Jing

摘要

This paper proposes an improved YOLOv5-based method for automatic detection of broken strand defects in distribution network conductors. To address challenges in complex environments and slender target localization, we introduce a lightweight neck network, channel attention mechanism, and an efficient feature extraction network (EFEN). Experiments on a self-built UAV inspection dataset of 1,364 images show that the improved model achieves 0.89 precision and 0.88 mAP, outperforming the original YOLOv5 by 6%. Field tests on over 1 km of power lines demonstrate an 88.4% detection rate with no false alarms.