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能源效率高的OPGW自动检测功能,以实现可持续的无人机检查
Xiaoling Yan1, Wuxing Mao1, Xiao Li1
1Guangzhou Electric Power Design Institute, Guangzhou 510630, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种改进的无人机 (UAV) 检查模型,用于检测光纤复合物空中地面电线 (OPGW). 新型号提高了小物体检测的准确性和电力线维护的效率.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 无人机 (UAV) 检查对于高压输电线路维护至关重要.
- 在复杂的图像中检测像光纤复合物上空接地线 (OPGW) 这样的小特征是具有挑战性的,因为照明和背景变化.
- 现有的检测方法对小物体缺乏准确性和效率,阻碍了无人机有效监测.
研究的目的:
- 为无人机检查开发一个节能的OPGW特征检测模型.
- 为了提高对OPGW检测至关重要的小和边缘特征的准确识别.
- 提高无人机检查系统的整体性能,降低无人机检查系统的计算需求.
主要方法:
- 提出了一种新型模型,将特征增强模块 (FEM) 集成到YOLO11骨干中,以实现卓越的多尺度特征提取.
- 整合了P2浅探测头,以提高对小目标和边缘目标的感知.
- 取代了交叉与联盟 (IoU) 损失的交叉与标准化瓦瑟斯坦距离 (NWD) 损失,以改进小物体的边界回归.
主要成果:
- 拟议的模型实现了78.3%的mAP50和52.0%的mAP50-95.
- 与基线相比显著改善,mAP50增加2.3%,mAP50-95.5增加1.1%.
- 该模型具有较高的检测精度,可减少计算资源需求.
结论:
- 开发的模型为基于无人机的电力线路检查中OPGW特征检测提供了实用和高效的解决方案.
- 整合FEM,P2头和NWD损失有效地解决了在复杂环境中检测小物体的挑战.
- 这一进步支持通过增强无人机能力来实现可持续和可靠的基础设施监控.
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