一个在线的11 kv配电系统绝缘器缺陷检测方法,采用修改后的YOLOv11和mobileNetV3
Arnav Bhagwat1, Soham Dutta2, Debdeep Saha3
1Department of Computer Science, University of Exeter,, Exeter, UK.
Scientific reports
|May 5, 2025
概括
这项研究引入了一种新的深度学习方法,用于在智能电网中使用无人机检测绝缘体缺陷. 这种方法提高了早期检测,防止停电和设备损坏.
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:
- 智能配电网需要强大的绝缘体缺陷检测,以防止输电和连续断电.
- 挑战包括复杂的背景,有限的数据集和小规模的缺陷检测.
- 深度学习为自动化绝缘体检验提供了一个有前途的解决方案.
研究的目的:
- 开发一种新的,轻量级的深度学习模型,用于检测11kV配电系统中的绝缘体缺陷.
- 用无人机 (UAV) 提高绝缘体缺陷检测的准确性和效率.
- 通过现有方法验证拟议模型的性能.
主要方法:
- 开发了一个修改后的YOLOv11架构,包含C3K2,SPFF和C2PSA块.
- 一个MobileNetV3分类器被集成为适合分布式自动化系统 (DAS) 的轻量级框架.
- 在预处理阶段采用了数据增强技术.
主要成果:
- 拟议的模型在现实场景中证明了在检测绝缘体缺陷方面的有效性.
- 性能比较显示出优越于早期的YOLO版本和其他现有方法.
- 案例研究证实了检测方法的稳定性和可靠性.
结论:
- 基于YOLOv11和MobileNetV3的新方法为绝缘体缺陷检测提供了高效和准确的解决方案.
- 这种轻量级的框架适合在DAS支持的设备上部署,用于自动化电网监控.
- 这种方法有助于提高智能配电网的可靠性和安全性.
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