使用YOLOv4和YOLOv8进行基于深度学习的电力传输线路检测
Tugce Nur Karadeniz1, Sami Ekici1, Engin Avci2
1Energy Systems Engineering, Firat University, Elazig, 23119, Turkey.
Scientific reports
|December 14, 2025
概括
无人驾驶飞行器 (UAV) 与YOLOv8深度学习模型相结合,显著提高了电力传输线路 (PTL) 检测. 这项技术为检查关键能源基础设施提供了更安全,更快,更准确的方法.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 电力输电线 (PTL) 的维护对于可靠的能源分配至关重要.
- 传统的PTL检查方法效率低下,成本高昂,并带来安全风险.
- 无人机技术为基础设施监控提供了更高的效率,安全性和成本效益.
研究的目的:
- 为了评估深度学习对象检测的有效性,特别是你只看一次 (YOLO) 算法,用于使用无人机进行PTL检查.
- 为了比较不同YOLO版本在检测电力传输线路方面的性能.
主要方法:
- 使用的无人机配备了用于空中数据采集的摄像头.
- 应用了YOLO算法的各种版本用于对象检测和PTL的分类.
- 分析的性能指标包括精度,回忆,F1得分,mAP50和mAP50-95.
主要成果:
- 所有测试的YOLOv8版本都在PTL检测方面表现优于YOLOv4.
- YOLOv8的精度,回忆和F1得分超过了99%.
- 平均mAP50和mAP50-95值分别为0.995和0.919记录在内.
结论:
- 基于深度学习的对象检测,使用与无人机集成的YOLOv8,为PTL监控提供了高度准确和高效的解决方案.
- YOLOv8显著优于之前的版本,为PTL检查技术建立了新的基准.
- 这种方法提高了能源分配网络的安全性和可靠性.
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