Aitelhaj Rita, Benelmostafa Badr-Eddine, Medromi Hicham
System Architecture Team (EAS), Engineering Research Laboratory (LRI),, National High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, Morocco, CASABLANCA, Morocco.
F1000Res. 2025 Jun 23;14:141. doi: 10.12688/f1000research.160650.2. eCollection 2025.
UAV-based power line inspections offer a safer, more efficient alternative to traditional methods, but insulator detection presents key challenges: multiscale object detection and intra-class variance. Insulators vary in size due to UAV altitude and perspective changes, while their visual similarities across types (e.g., glass, porcelain, composite) complicate classification.
To address these issues, we introduce APF-YOLO, an enhanced YOLOv8-based model integrating the Adaptive Path Fusion (APF) neck and the Adaptive Feature Alignment Module (AFAM). AFAM balances fine-grained detail extraction for small objects with semantic context for larger ones through local and global pathways by integrating advanced attention mechanisms. This work also introduces the Merged Public Insulator Dataset (MPID), a comprehensive dataset designed for insulator detection, representing diverse real-world conditions such as occlusions, varying scales, and environmental challenges.
Evaluations on MPID demonstrate that APF-YOLO surpasses state-of-the-art models with different neck configurations, achieving at least a +2.71% improvement in mAP@0.5:0.9 and a +1.24% increase in recall, while maintaining real-time performance in server-grade environments. Although APF-YOLO adds computational requirements, these remain within acceptable limits for real-world applications. Future work will optimize APF-YOLO for edge devices through techniques such as model pruning and lightweight feature extractors, enhancing its adaptability and efficiency.
Combined with MPID, APF-YOLO establishes a strong foundation for advancing UAV-based insulator detection, contributing to safer and more effective power line monitoring.
基于无人机的电力线巡检提供了一种比传统方法更安全、更高效的替代方案，但绝缘子检测存在关键挑战：多尺度目标检测和类内差异。由于无人机高度和视角变化，绝缘子尺寸各异，而不同类型（如玻璃、陶瓷、复合）绝缘子的视觉相似性使分类变得复杂。
为解决这些问题，我们引入了APF-YOLO，这是一种基于YOLOv8的增强模型，集成了自适应路径融合（APF）颈部和自适应特征对齐模块（AFAM）。AFAM通过集成先进的注意力机制，通过局部和全局路径平衡小目标的细粒度细节提取和大目标的语义上下文。这项工作还引入了合并公共绝缘子数据集（MPID），这是一个为绝缘子检测设计的综合数据集，代表了各种现实世界条件，如遮挡、不同尺度和环境挑战。
在MPID上的评估表明，APF-YOLO超过了具有不同颈部配置的现有模型，在mAP@0.5:0.9上至少提高了2.71%，召回率提高了1.24%，同时在服务器级环境中保持实时性能。虽然APF-YOLO增加了计算需求，但这些需求在现实世界应用中仍在可接受范围内。未来的工作将通过模型剪枝和轻量级特征提取器等技术对边缘设备的APF-YOLO进行优化，提高其适应性和效率。
结合MPID，APF-YOLO为推进基于无人机的绝缘子检测奠定了坚实基础，有助于实现更安全、更有效的电力线监测。
