基于全面的电力配电网络故障检测-YOLOv5
Shengsuo Niu1, Xiaosen Zhou1, Dasen Zhou1
1Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power University, Baoding 071003, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了Comprehensive-YOLOv5,这是一种用于实时检测发电网中的缺陷的轻量级算法. 它显著提高了准确性和速度,使得边缘设备的部署更高效.
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:
- 在配电网络中实时故障检测至关重要,但由于边缘设备的局限性而受到挑战.
- 现有的方法与边缘设备的低功耗和计算限制作斗争.
研究的目的:
- 提出一种轻量级算法,Comprehensive-YOLOv5,用于高效准确地识别发电网中的缺陷.
- 为了增强边缘设备的实时检测能力.
主要方法:
- 开发了基于YOLOv5的Comprehensive-YOLOv5,并将GhostNet纳入骨干重建.
- 综合双向特征金字塔网络 (BiFPN) 改善了特征融合.
- 使用焦点扩展交叉点在欧盟 (Focal-EIOU) 损失函数,以提高性能.
主要成果:
- 综合YOLOv5实现了90.1%的平均平均精度 (mAP),比88.3%的平均精度有所改善.
- 算法的速度增加到每秒52 (FPS),从20 FPS上升.
- 在缺陷检测中,证明了虚假阳性和虚假阴性的显著减少.
结论:
- 综合YOLOv5为发电网的实时缺陷检测提供了一个可行的解决方案.
- 该算法的轻量级设计和改进的性能使其适合于边缘部署.
- 提高检测速度和准确性提高了整体检查效率.
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