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一个实例细分网络,用于在油浸式变压器内释放碳痕迹,并增强边界和细节特征
Hongxin Ji1, Jiaqi Li2, Peilin Han2
1School of Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China. jihongx816@163.com.
Scientific reports
|September 1, 2025
概括
这项研究引入了一个新的AI网络,CDEP-CTSeg,用于使用微机器人检测浸油变压器的绝缘缺陷. 网络显著提高了碳痕迹的准确性和速度,提高了变压器的安全性.
科学领域:
- 电气工程
- 人工智能
- 机器人技术
背景情况:
- 在大型浸油变压器中检测内部绝缘缺陷是具有挑战性的,因为它们的结构封闭.
- 现有的方法难以应对各种形状,尺寸和复杂的缺陷背景,
研究的目的:
- 使用微机器人开发自动视觉检查系统,用于识别变压器的绝缘缺陷.
- 提出一个新的深度学习网络,即CDEP-CTSeg,用于准确有效地细分碳痕迹.
主要方法:
- 改进的Retinex算法用于图像预处理,以处理过度曝光和色彩扭曲.
- 一个新的C2f-DySnake模块被集成到YOLOv8框架中,以改善复杂的分支碳痕迹的细分.
- 一个EHSPAN特征金字塔网络被纳入以增强小碳痕迹的检测.
主要成果:
- 与YOLOv8-seg模型相比,CDEP-CTSeg网络显示了更好的回忆 (2.3%),精度 (2.8%) 和mAP50 (2.8%).
- 在聚类 (1.9%) 和树突性 (3.7%) 碳痕迹的平均精度中观察到显著的改善.
- 建议的方法可以快速准确地对变压器内部绝缘缺陷进行细分.
结论:
- CDEP-CTSeg网络有效地解决了油浸式变压器中检测和细分绝缘缺陷的挑战.
- 这项技术有助于稳定运行和预测性维护大型浸油变压器.
- 开发的系统为关键电气基础设施的自动化内部视觉检查提供了一个有前途的解决方案.
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