一个基于指标学习的改进定向的R-CNN用于在电力传输走廊中检测野火
Xiaole Wang1, Bo Wang1, Peng Luo1
1School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种改进的定向R-CNN模型,用于在电力传输走廊中检测野火. 改进的模型显著提高了识别烟雾和火焰的准确性,确保了电力线的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 在输电走廊中检测野火对于电网稳定性和安全性至关重要.
- 挑战包括区分烟雾和背景杂乱,多样化的目标外观,以及检测小型烟雾/火焰物体.
- 现有的方法在复杂的环境中难以准确.
研究的目的:
- 开发一种先进的物体检测模型,用于在电力传输通道中准确检测野火.
- 改进小规模烟雾和火焰目标的识别.
- 提高电力传输基础设施的整体稳定性和安全性.
主要方法:
- 提出了一个改进的面向R-CNN模型,该模型包含了度量学习.
- 引入了一个多中心度量损失 (MCM-Loss) 模块,以改善特征差异化.
- 整合了ResNeXt和FPN-CARAFE模块,以增强特征提取和多尺度表示.
主要成果:
- 该MCM-Loss模块提高了2.7%的烟雾目标平均精度 (AP).
- 将ResNet替换为ResNeXt将平均平均精度 (mAP) 提高了0.6%.
- 在FPN-CARAFE模块增加了8.1%的火目标AP,达到90.4%的最终mAP (6.4%的改进).
结论:
- 拟议的模型在电力传输走廊中展示了在野火检测方面卓越的性能.
- 计量学习和高级网络模块的整合有效地解决了检测挑战.
- 这项研究为野火监测和电网安全提供了宝贵的支持.
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