使用基于改进的YOLOv5的无人机成像进行小型绝缘体和缺陷检测的轻量级网络
Tong Zhang1, Yinan Zhang1, Min Xin1
1College of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究引入了一种轻量级网络,用于检测电力线上的小型绝缘体缺陷,提高无人机的检测准确度和速度. 改进后的模型显著降低了计算负载,使其成为实时缺陷识别的理想选择.
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:
- 电力传输线路的稳定性严重依赖于有效的绝缘体缺陷检测.
- 现有的物体检测网络,如YOLOv5,由于检测速度差和计算需求高,难以检测小型绝缘体缺陷.
- 无人驾驶飞行器 (UAV) 为电力线检查提供了一个有前途的平台,但需要高效的检测模型.
研究的目的:
- 开发一个轻量级和高效的网络,用于检测小型绝缘体缺陷.
- 为了提高检测准确度,减少绝缘体缺陷检测系统的计算负载.
- 使用无人机实现实时缺陷检测.
主要方法:
- 将Ghost模块引入YOLOv5的骨干和部,以减少参数和模型大小.
- 嵌入了小型物体检测和层,以增强小型缺陷检测能力.
- 使用卷积块注意模块 (CBAM) 优化了YOLOv5骨干,以更好地关注关键缺陷信息.
主要成果:
- 在IOU0.5时达到99.4%的平均平均精度 (mAP),在IOU0.5-0.95.5时达到91.7%.
- 模型参数减少到3,807,372个,模型大小减少到8.79M.
- 实现了10.9ms/图像的检测速度,满足了实时检测要求.
结论:
- 拟议的轻量级网络有效地检测出小型绝缘体缺陷,具有高准确性和效率.
- 该模型的缩小尺寸和计算负载有助于在无人机等嵌入式设备上部署.
- 这一进步通过增强的实时缺陷监控来支持稳定的电力输电线路运行.
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