自动诊断电缆的装满油的终端中的加热情况
Shunyu Yao1,2, Zhen Liu3, Yong Zhao3
1State Grid Henan Electric Power Company, Zhengzhou Power Supply Company, Zhengzhou, 450000, China. 1145652959@qq.com.
Scientific reports
|November 5, 2025
概括
一种基于无人机的新方法使用融合的红外和可见图像来自动检测电缆终端加热. 与手动方法相比,这种技术显著提高了检测效率和精度.
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:
- 充满电缆油的终端容易加热,造成故障的风险.
- 传统的手动诊断方法耗时,可能缺乏精度.
研究的目的:
- 开发一个自动诊断技术,用于电缆油填充终端加热.
- 为了提高加热检测的效率和精度.
主要方法:
- 使用DJI无人机收集红外和可见光图像.
- 使用尺度不变特征转换 (SIFT) 注册的图像.
- 使用渐进式红外和可见图像融合网络 (PIA Fusion) 融合的图像.
- 训练了你只看一次版本5 (YOLOv5) 网络用于对象检测.
- 使用DJI红外分析软件工具包 (TSDK) 进行温度分析.
主要成果:
- 在与融合图像进行训练时,获得了95.3%的平均平均精度 (mAP@0.5).
- 每张图像的平均检测时间为12ms.
- 准确地识别和绘制了易受加热的区域.
结论:
- 提出的自动诊断技术有效地检测到电缆终端加热.
- 融合图像训练提高了检测精度和效率.
- 这种方法为传统的手动诊断提供了可行的替代方案.
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