基于RB-UNet算法的传输线异物细分
Yan Wang1, Qinghe Yuan2, Ying Wang1
1Economic and Technological Research Institute, State Grid Heilongjiang Electric Power Co. Ltd., Harbin, Heilongjiang, China.
PeerJ. Computer science
|December 9, 2024
概括
一个新的RB-UNet模型有效地检测输电线路上的外来物体,达到88.43%mIoU. 这种先进的语义细分技术提高了电力基础设施的运营安全性和效率.
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:
- 在输电线路上识别异物对于运营完整性至关重要.
- 挑战包括分散的分布和高度高,带来风险和运营困难.
研究的目的:
- 开发一个先进的语义细分网络,用于在传输线路上准确检测异物.
- 改进现有的加强电力线路监控和安全的方法.
主要方法:
- 推出了RB-UNet,一个新的语义细分网络.
- 通过将编码器替换为ResNet50进行功能提取,增强了U-Net.
- 在解码器中内置批量规范化和联合焦点和子损失功能,以解决类不平衡并提高准确性.
主要成果:
- 实现了外来物体检测的88.43%的欧盟 (mIoU) 的平均交叉点.
- 在其他语义细分技术上表现出显著的优势.
- RB-UNet在检测和细分各种外来物体 (如,,气球和垃圾) 方面表现出了很好的表现.
结论:
- 拟议的RB-UNet算法为传输线路上的外来物体检测提供了一个高度有效的解决方案.
- 增强的架构和损失功能有助于在具有挑战性的监控场景中提供卓越的性能.
- 这项技术提高了输电基础设施的安全性和可靠性.
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