一种基于前置背景分离的无训练导线提取方法,用于大格式传输线路图像
Ning Liu1,2, Yuncan Bai1,2, Jingru Liu1,2
1State Grid Electric Power Space Technology Company Limited, Beijing 102209, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种新的,无需培训的方法,用于从复杂图像中提取传输线,这对于智能电网监控至关重要. 该方法使用深度估计来改进电线分离和识别,减少计算负载.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 智能电网需要对输电线路进行先进的监控.
- 精确的导线提取对于大格式图像中缺陷检测至关重要.
- 现有的方法通常需要大量的训练数据,并与复杂的背景作斗争.
研究的目的:
- 开发一种无需培训的导线提取方法,用于大规模的输电线图像.
- 通过使用深度估计,增强线路与复杂背景的分离.
- 为电线识别提供高效且独立于数据集的解决方案.
主要方法:
- 利用深度估计图来改善前景线和背景线的分离.
- 采用基于线段结构的方法来识别水平定向的线性特征.
- 开发一种不需要培训和数据集独立的方法.
主要成果:
- 有效地将传输电线与复杂的背景进行分离.
- 在杂乱的场景中,精细的电线结构的稳健识别.
- 与深度学习方法相比,减少了计算开销.
结论:
- 拟议的无培训方法准确地从具有挑战性的图像中提取传输线.
- 深度估计显著提高了电线背景的分离性.
- 这种方法为智能电网输电线路监控提供了有效的替代方案.
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