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<p>To address the issues of low detection accuracy and insufficient model lightweighting in UAV-based transmission line foreign object detection, this paper proposes SDS-YOLOv8n, an optimized foreign object detection algorithm. Built upon the YOLOv8n architecture, the proposed model incorporates three targeted improvements to balance detection accuracy and computational efficiency. First, an Enhanced SPPF module integrating parallel global average and max pooling layers is designed to improve the model’s focus on target edge details while suppressing environmental background interference. Second, the standard detection head is replaced with a Dynamic Detection Head that unifies scale-aware, spatial-aware, and task-aware attention mechanisms, significantly enhancing feature adaptability for irregular objects such as kites and bird nests. Third, a Parameter-free Attention Mechanism (SimAM) is embedded to further refine feature extraction without increasing model parameters. Experimental results on a self-constructed dataset demonstrate that SDS-YOLOv8n achieves a mAP@0.5 of 95.8% and a mAP@0.5:0.95 of 75.1%, outperforming the baseline model by 1.1% and 2.6%, respectively. Furthermore, the model exhibits strong generalization capabilities on an additional untrained dataset. With a parameter count of 2.75 M and high inference speed verified on the NVIDIA Jetson Orin Nano platform, the proposed method provides a robust and efficient solution for real-time intelligent grid inspection.</p>

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SDS-YOLO: drone-based foreign object detection model for power lines using an enhanced YOLOv8n approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Jing Sheng,
  • Shuliang Wu,
  • Guoman Liu,
  • Xin Ma,
  • Shunhu Deng,
  • Hao Xu

摘要

To address the issues of low detection accuracy and insufficient model lightweighting in UAV-based transmission line foreign object detection, this paper proposes SDS-YOLOv8n, an optimized foreign object detection algorithm. Built upon the YOLOv8n architecture, the proposed model incorporates three targeted improvements to balance detection accuracy and computational efficiency. First, an Enhanced SPPF module integrating parallel global average and max pooling layers is designed to improve the model’s focus on target edge details while suppressing environmental background interference. Second, the standard detection head is replaced with a Dynamic Detection Head that unifies scale-aware, spatial-aware, and task-aware attention mechanisms, significantly enhancing feature adaptability for irregular objects such as kites and bird nests. Third, a Parameter-free Attention Mechanism (SimAM) is embedded to further refine feature extraction without increasing model parameters. Experimental results on a self-constructed dataset demonstrate that SDS-YOLOv8n achieves a mAP@0.5 of 95.8% and a mAP@0.5:0.95 of 75.1%, outperforming the baseline model by 1.1% and 2.6%, respectively. Furthermore, the model exhibits strong generalization capabilities on an additional untrained dataset. With a parameter count of 2.75 M and high inference speed verified on the NVIDIA Jetson Orin Nano platform, the proposed method provides a robust and efficient solution for real-time intelligent grid inspection.