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通过YOLO-NAS深度学习模型自动检测容器损坏.

Thanh Nguyen Thi Phuong1, Gyu Sung Cho1, Indranath Chatterjee2,3,4

  • 1Department of Port Logistics System, Tongmyong University, Busan, Republic of Korea.

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|January 31, 2025
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概括

使用YOLO-NAS的自动化运输集装箱损坏检测实现了高精度,优于其他模型. 这项技术提高了智能港口的物流效率和安全性.

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集装箱的损坏 集装箱的损坏你只看一次 神经架构搜索 (YOLO-NAS)计算机视觉 计算机视觉深度学习是一种深度学习.物流系统的物流系统.对象检测检测对象检测对象检测港口效率 港口效率 港口效率 港口效率 港口效率风险分析 风险分析

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 供应链管理 供应链管理

背景情况:

  • 手动检查运输集装箱是低效的,容易出现错误,成本高昂.
  • 损坏的容器对产品质量,物流和安全构成风险.
  • 为了高效的港口运营,需要自动化解决方案.

研究的目的:

  • 引入和评估用于自动检测集装箱损坏的YOLO-NAS模型.
  • 为满足复杂港口环境中高速,高精度检查的需求.
  • 将YOLO-NAS性能与其他领先的物体检测模型进行比较.

主要方法:

  • 实施用于对象检测的YOLO-NAS深度学习模型.
  • 模型的应用以检测各种类型的集装箱损坏.
  • 使用YOLOv8,Roboflow 3.0,Fmask-RCNN和MobileNetV2.2进行比较分析.

主要成果:

  • 约洛-纳斯的平均精度 (mAP) 为91.2%,精度为92.4%,回忆率为84.1%.
  • 与YOLOv8和Roboflow 3.0.0相比,YOLO-NAS表现出了更高的性能.
  • 与其他模型不同,YOLO-NAS提供了对港口物流至关重要的实时评估功能.

结论:

  • YOLO-NAS在海港自动检测集装箱损坏方面非常有效.
  • 该模型提高了物流效率,降低了成本,并提高了安全性.
  • 这项技术支持智能端口和预测性维护系统的开发.