从小数据样本检测绝缘体异常状况
Qian Wang1, Zhixuan Fan1, Zhirong Luan1
1School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了一种人工智能驱动的方法,用于使用无人机检测绝缘体缺陷,通过数据增强克服小样本大小的限制. 这种方法可以提高更安全的发电网的检测准确性.
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:
- 绝缘体性能对于电力系统的可靠性和安全性至关重要.
- 传统的绝缘体检查方法是劳动密集型和资源密集型的.
研究的目的:
- 开发一种基于人工智能的方法,使用无人机视觉传感器检测异常绝缘体状况.
- 为了解决用于绝缘体检查的图像数据中小样本大小的挑战.
主要方法:
- 利用数据增强技术来扩展有限的绝缘体图像数据集.
- 应用了YOLOV5算法来检测异常情况.
- 在数据集优化之前和之后比较检测性能.
主要成果:
- 数据增强显著提高了绝缘体图像数据集的可靠性和通用性.
- 通过扩展数据集,YOLOV5算法证明了更高的检测准确度和精度.
- 拟议的方法有效地解决了无人机检查中的小样本问题.
结论:
- 基于人工智能的无人机检查方法为传统的绝缘体检测提供了可行的替代方案.
- 该研究为主动配电网络安全提供了理论指导和实际应用前景.
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