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轨道紧固件缺陷检测模型基于改进的YOLOv5s.

Xue Li1, Quan Wang1, Xinwen Yang2

  • 1School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

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|July 29, 2023
PubMed
概括

本研究介绍了一种改进的YOLOv5s模型,用于高效地检测铁路紧固件缺陷. 改进后的模型实现了高精度和速度,支持智能铁路检查.

关键词:
这是BiFPN BiFPN.这是YOLOv5s.注意力机制注意力机制数据增强 增强数据增强检测缺陷检测检测缺陷检测的方法紧固件 紧固件是一种紧固件.这是一条道路,一条轨道,一条道路.

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科学领域:

  • 铁路工程 铁路工程 铁路工程
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 对于现代铁路网络来说,传统的铁路紧固件手工检查是不够的.
  • 自动缺陷检测对于确保铁路安全和运行可靠性至关重要.

研究的目的:

  • 开发一个准确,快速和智能的铁路紧固件缺陷检测模型.
  • 改进现有的铁路基础设施检查目标检测模型.

主要方法:

  • 一个改进的YOLOv5s模型,包括卷积块注意模块 (CBAM) 和加权双向特征金字塔网络 (BiFPN).
  • 使用K-means++算法,以根据紧固件数据集量身定制的最佳箱选择.
  • 实施多级特征融合和增强特征提取技术.

主要成果:

  • 在缺陷检测方面获得了97.4%的平均平均精度 (mAP).
  • 显示了每秒27.3 (FPS) 的检测速度.
  • 保持了一个小的模型内存占用率15.5 MB.

结论:

  • 与现有方法相比,改进的YOLOv5s模型在准确性,速度和效率方面提供了卓越的性能.
  • 该模型为实时轨道紧固件缺陷检测的边缘部署提供了强大的技术支持.
  • 这一进步有助于通过智能检查实现更安全,更可靠的铁路运行.