轻量级输电线路故障检测方法基于更轻的YOLOv7-Tiny
Qingyan Wang1, Zhen Zhang1, Qingguo Chen1
1School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
一个新的轻量级模型,Leaner YOLOv7-Tiny,通过使用空中图像来增强输电线路故障检测. 它大大减少了模型大小,提高了准确性,特别是对于小目标,使无人机部署成为可能.
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:
- 现有的输电线故障检测算法存在高参数复杂性和计算负载问题.
- 这些局限性阻碍了在无人机等资源有限的平台上部署故障检测系统.
- 精确有效地检测输电线路故障对于电网可靠性至关重要.
研究的目的:
- 提出一种新的轻量级物体检测模型,Leaner YOLOv7-Tiny,用于从空中图像中快速准确地检测输电线路故障.
- 为了减少模型大小和实际无人机应用的计算要求.
- 为了提高小型和典型的输电线路故障的检测准确度.
主要方法:
- 通过修改YOLOv7-Tiny架构开发了精益型YOLOv7-Tiny.
- 用深度可分离的卷积取代了脊柱,以减少模型参数.
- 集成的SP注意力机制用于多尺度特征融合和改进的小目标识别.
- 引入了改进的FCIoU损失函数,以平衡样本贡献并加速趋同.
主要成果:
- 与原来的YOLOv7-Tiny.相比,模型尺寸减少了20%.
- 与现有的主流轻量级物体检测算法相比,对小目标的检测准确度证明更高.
- 该模型表现出增强的检测速度和传输线路故障的准确性.
结论:
- 精益YOLOv7-Tiny为传输线路故障检测提供了一个实用和高效的解决方案.
- 轻量级的设计和提高的准确性使其适用于基于无人机的检查系统.
- 这种方法显著提高了实时监控和维护电力传输基础设施的能力.
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