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Visual inspection remains a common approach for assessing composite insulators, with unmanned aerial vehicles (UAVs) increasingly preferred due to their efficiency and reduced error rates. Recent developments have integrated artificial intelligence (AI) algorithms directly into UAV hardware to enable faster processing; however, such systems require optimized models owing to limited onboard computing resources. The recently introduced YOLOv10-N model, which offers greater efficiency compared to its predecessors, demonstrates potential for detecting insulator defects on resource-constrained UAV platforms. This study evaluates the effectiveness of YOLOv10-N for this application.

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Detection of Power Line Insulator Defects Using YOLOv10-N

  • 会议论文

  • 发表日期：

    2026年

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  • Kgampu Shawn Papi,
  • Terence L. van Zyl

摘要

Visual inspection remains a common approach for assessing composite insulators, with unmanned aerial vehicles (UAVs) increasingly preferred due to their efficiency and reduced error rates. Recent developments have integrated artificial intelligence (AI) algorithms directly into UAV hardware to enable faster processing; however, such systems require optimized models owing to limited onboard computing resources. The recently introduced YOLOv10-N model, which offers greater efficiency compared to its predecessors, demonstrates potential for detecting insulator defects on resource-constrained UAV platforms. This study evaluates the effectiveness of YOLOv10-N for this application.