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<p>The increasing demand for intelligent operation and maintenance in hydropower plants highlights the limitations of conventional inspection approaches in real-time performance and reliability, especially under edge-computing constraints. This paper presents DRR-YOLOv11s, a lightweight object detector for electrical equipment fault detection and worker safety monitoring. By optimizing the model structure for efficient inference, DRR-YOLOv11s reduces parameters by 37.58% and computational cost by 28.64% compared with the baseline, resulting in 5.88&#xa0;M parameters and 15.2 GFLOPs. Experiments on the Electrical Equipment Failure dataset show a precision of 92.50%, recall of 89.42%, mAP@0.5 of 93.46%, and mAP@0.5:0.95 of 73.45%, while achieving 160.22 FPS. Cross-dataset evaluation on a Personal Protective Equipment dataset further indicates good generalization. Overall, DRR-YOLOv11s achieves a favorable trade-off between accuracy and efficiency, supporting practical deployment for real-time monitoring in hydropower plants.</p>

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A lightweight DRR-YOLOv11s model for power equipment failure and personal protective equipment detection in hydropower stations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Ping Yu,
  • Baoshu Zong,
  • Xiaozhong Geng,
  • Shuaiting Chen,
  • Yong Li,
  • Xiaoqing Xu

摘要

The increasing demand for intelligent operation and maintenance in hydropower plants highlights the limitations of conventional inspection approaches in real-time performance and reliability, especially under edge-computing constraints. This paper presents DRR-YOLOv11s, a lightweight object detector for electrical equipment fault detection and worker safety monitoring. By optimizing the model structure for efficient inference, DRR-YOLOv11s reduces parameters by 37.58% and computational cost by 28.64% compared with the baseline, resulting in 5.88 M parameters and 15.2 GFLOPs. Experiments on the Electrical Equipment Failure dataset show a precision of 92.50%, recall of 89.42%, mAP@0.5 of 93.46%, and mAP@0.5:0.95 of 73.45%, while achieving 160.22 FPS. Cross-dataset evaluation on a Personal Protective Equipment dataset further indicates good generalization. Overall, DRR-YOLOv11s achieves a favorable trade-off between accuracy and efficiency, supporting practical deployment for real-time monitoring in hydropower plants.