改良型YOLOv11に基づく送電線個人用保護具検出アルゴリズムの研究

In intelligent transmission line inspection, edge computing devices face the critical challenge of balancing real-time performance with detection accuracy for personal protective equipment recognition in complex operational scenarios. This study proposes WTLS-YOLOv11n, a lightweight and inherently interpretable detection algorithm. The methodology integrates three synergistic innovations into the YOLOv11n architecture. A C3K2-WTConv module employing a discrete wavelet transform decomposes features into physically meaningful frequency components, enabling robust multi-scale feature extraction with inherent interpretability. A lightweight shared composite detection head achieves substantial parameter reduction through strategic weight sharing while preserving multi-scale fusion capabilities. The MPDIoU loss function enhances localization accuracy for small and irregularly shaped targets. Experimental validation demonstrates that the proposed model achieves superior detection accuracy with significantly reduced parameters and computational complexity compared to the baseline, while maintaining real-time inference performance on edge hardware platforms. Quantitative interpretability analysis reveals that detection decisions are predominantly driven by low-frequency structural features rather than high-frequency textural details, providing transparent insight into the model's reasoning process. Comparative experiments against mainstream detection models validate the superior accuracy-efficiency trade-off of the proposed approach. This work establishes a transparent, efficient, and trustworthy solution for automated safety supervision in electrical power operations, with broader applicability to safety-critical detection tasks requiring edge deployment and interpretable decision-making.