在GIS X射线图像中的缺陷检测基于改进的YOLOv10
Guoliang Xu1, Xiaolong Bai1, Menghao Huang1
1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种先进的AI模型,用于使用X射线图像检测气体绝缘开关设备 (GIS) 的内部缺陷. 增强的YOLOv10n模型显著提高了识别小,低对比度缺陷的准确性,提高了电力系统的可靠性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 气体绝缘开关装置 (GIS) 的内部缺陷对电力系统可靠性构成风险.
- 自动化X射线缺陷检测是具有挑战性的,因为小,低对比度的缺陷和复杂的背景.
研究的目的:
- 为准确的GIS缺陷识别开发一个增强的对象检测模型.
- 改进在GIS X射线图像中检测小和低对比度的缺陷.
主要方法:
- 使用轻量级YOLOv10n框架,增强了用于小物体定位的规范瓦瑟斯坦距离 (NWD) 损失.
- 集成的蒙特卡洛 (MCAttn) 和并行补丁意识 (PPA) 注意机制,以改善特征提取.
- 采用GFPN灵感的子来实现有效的多尺度特征融合.
主要成果:
- 改进的模型实现了0.674的平均平均精度 (mAP) (IoU 0.5:0.95).
- 与基线YOLOv10n模型相比,显示了5.0个百分点的改善.
- 在检测具有挑战性的缺陷方面表现优于其他比较型号.
结论:
- 拟议的模型为自动化GIS缺陷检测提供了有效的解决方案.
- 显著的潜力提高电网维护效率和安全.
- 定性结果证实了小和低对比度缺陷的优异检测.
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