APF-YOLOV8:提高无人机绝缘电线检查的多尺度检测和类内偏差处理
Rita Aitelhaj1, Badr-Eddine Benelmostafa1, Hicham Medromi1
1System Architecture Team (EAS), Engineering Research Laboratory (LRI),, National High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, Morocco, CASABLANCA, Morocco.
F1000Research
|September 2, 2025
概括
通过加强绝缘体检测,APF-YOLO可以改善无人机的电力线路检查. 这种先进的模型与合并的公共绝缘器数据集相结合,提高了关键基础设施监测的准确性和效率.
科学领域:
- 计算机视觉
- 人工智能
- 电气工程
背景情况:
- 基于无人机 (UAV) 的电力线路检查在绝缘体检测方面面临挑战,包括不同物体尺寸和绝缘体类型之间的视觉相似性.
- 传统的检查方法通常与自动无人机方法相比,安全性和效率较差.
研究的目的:
- 开发一个先进的深度学习模型,用于基于无人机的电力线路检查中准确有效地检测绝缘体.
- 引入一个全面的数据集,用于在各种现实条件下训练和评估绝缘体检测模型.
主要方法:
- 推出了基于YOLOv8的APF-YOLO模型,其中包含了自适应路径融合 (APF) 和自适应特征调整模块 (AFAM).
- AFAM使用本地和全球通道与注意力机制来平衡不同尺度对象的特征提取.
- 开发了合并公共绝缘体数据集 (MPID),以满足各种培训数据的需求,包括闭塞和尺度变化.
主要成果:
- 在MPID上,APF-YOLO表现优于最先进的模型,在mAP@0.5:0.9上增加了2.71%,在回忆中增加了1.24%.
- 该模型保持了适合实际应用的实时性能,尽管计算需求略有增加.
- 评估证实了该模型在处理现实世界电线环境中固有的阻塞和尺度变化等挑战方面的有效性.
结论:
- APF-YOLO与MPID一起,为基于无人机的绝缘体检测提供了强大的解决方案,提高了电力线监控的安全性和效率.
- 开发的模型代表了自动化基础设施检查技术的重大进步.
- 未来的研究将专注于优化APF-YOLO的边缘设备,以进一步扩大其适用性.
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