基于YOLOv5s对象检测网络的绝缘拉杆缺陷数据集的预处理方法
Xuetong Li1, Meng Cong2, Bo Liu2
1Department of Electrical Engineering, Tsinghua University, Beijing 100084, China.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了一种数据预处理技术,以改善气体绝缘开关设备 (GIS) 部件的缺陷检测. 通过提高小缺陷的可见性,该方法显著提高了绝缘故障智能识别系统的性能.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
背景情况:
- 气体绝缘开关设备 (GIS) 组件,特别是绝缘拉杆,容易产生生产过程中的微缺陷.
- 绝缘故障的智能识别方法需要大型,平衡的数据集,由于缺陷样本有限和缺陷类型不平衡,因此很难获得这些数据集.
- 当前的方法在面对不平衡的缺陷数据时,在识别性能较差的情况下扎.
研究的目的:
- 为绝缘拉杆缺陷特征数据集提出有效的数据预处理方法.
- 为了提高对绝缘拉杆缺陷的智能识别系统的识别性能.
- 为了应对有限的缺陷样本和实际生产数据中不平衡的缺陷类别的挑战.
主要方法:
- 利用YOLOv5s算法在绝缘拉杆图像中检测缺陷,建立了一个包含五个缺陷类别的数据集.
- 引入了两种预处理技术:在图像中复制粘贴增强和对类似毛发的杂质进行边界框校正.
- 集成的复制粘贴增强与马赛克数据增强和精细的边界框纠正毛状杂质.
主要成果:
- 拟议的预处理方法有效地提高了小型缺陷目标 (杂质和泡),同时保持了其他缺陷类型的检测性能.
- 复制粘贴增强,马赛克增强和边界框校正的综合方法显著改善了整体模型性能.
- 对于小尺寸的缺陷目标展示了特殊的增强,这对于准确的故障识别至关重要.
结论:
- 开发的数据预处理方法有效地提高了GIS中绝缘拉杆缺陷检测模型的性能.
- 集成特定的增强和纠正技术解决了不平衡的缺陷数据的问题,导致更强大的识别系统.
- 这种方法为提高电气设备智能故障识别可靠性的可行解决方案.
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