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<p>Transmission lines are critical infrastructure for power delivery, yet their exposure to harsh environmental conditions accelerates component degradation, leading to defects that threaten grid reliability. While UAV-assisted inspections enable efficient defect identification, existing automated detection models face persistent challenges including severe class imbalance and complex environmental interference. To address these challenges, we first analyze a real-world 5,300-image aerial dataset, demonstrating severe class imbalance and diverse environmental noise in detail. We then propose a three-pronged solution: (1) a data augmentation pipeline integrating random occlusion and mirroring to enhance rare defect samples; (2) the Attention-Enhanced Multiple Component Defects Detection (AE-MCDD) model, combining HgNetV2 for local feature extraction, a Hybrid Attention Transformer (HAT) module for global context modeling, and C2F modules with skip connections for multi-scale feature fusion; and (3) a focal-loss-optimized multi-task loss function to handle class imbalance. Extensive experiments on our real-world dataset demonstrate that the proposed AE-MCDD model achieves a 0.719 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12083_2025_2079_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="51" /> </InlineMediaObject> <EquationSource Format="TEX">\(m\text {AP}_{50}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>m</mi> <msub> <mtext>AP</mtext> <mn>50</mn> </msub> </mrow> </math></EquationSource> </InlineEquation>, outperforming baseline methods in both common and rare defect detection.</p>

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AE-MCDD: Attention-enhanced multiple component defects detection for UAV-assisted powerline inspection

  • 期刊论文

  • 发表日期：

    2025年8月9日

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  • Jiehao Li,
  • Manjia Liu,
  • Haitao Peng,
  • Longlong Liu,
  • Xiaomin Zheng,
  • Chen Yi,
  • Guozi Liu,
  • Jieyu Zhou,
  • Feng Lyu

摘要

Transmission lines are critical infrastructure for power delivery, yet their exposure to harsh environmental conditions accelerates component degradation, leading to defects that threaten grid reliability. While UAV-assisted inspections enable efficient defect identification, existing automated detection models face persistent challenges including severe class imbalance and complex environmental interference. To address these challenges, we first analyze a real-world 5,300-image aerial dataset, demonstrating severe class imbalance and diverse environmental noise in detail. We then propose a three-pronged solution: (1) a data augmentation pipeline integrating random occlusion and mirroring to enhance rare defect samples; (2) the Attention-Enhanced Multiple Component Defects Detection (AE-MCDD) model, combining HgNetV2 for local feature extraction, a Hybrid Attention Transformer (HAT) module for global context modeling, and C2F modules with skip connections for multi-scale feature fusion; and (3) a focal-loss-optimized multi-task loss function to handle class imbalance. Extensive experiments on our real-world dataset demonstrate that the proposed AE-MCDD model achieves a 0.719 \(m\text {AP}_{50}\) m AP 50 , outperforming baseline methods in both common and rare defect detection.