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DSYOLO-trash:一个注意力机制集成和对象跟踪算法用于固体废物检测.

Wanqi Ma1, Hong Chen1, Wenkang Zhang2

  • 1School of Business, Jiangnan University, Wuxi 214122, PR China; Research Institute of National Security and Green Development, Jiangnan University, Wuxi 214122, PR China.

Waste management (New York, N.Y.)
|February 20, 2024
PubMed
概括

研究人员开发了DSYOLO-Trash,这是一种用于实时固体废物识别和跟踪的智能系统. 这种先进的模型简化了回收利用,有助于可持续的城市废物管理.

关键词:
注意力机制注意力机制对象检测检测对象检测对象检测废物检测检测器的废物检测系统废物管理 废物管理

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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 城市固体废物生产在全球范围内随着生活水平的变化而变化,这带来了各种管理挑战.
  • 当前的废物回收标准往往太复杂,无法让公众遵守.
  • 有效的废物管理对于城市的可持续性至关重要.

研究的目的:

  • 开发一个智能系统,用于准确和实时的固体废物识别和跟踪.
  • 简化废物分类,提高公众回收利用的可用性.
  • 为可持续城市提供智能废物管理解决方案.

主要方法:

  • 拟议的DSYOLO-垃圾模型整合了双重注意力机制:卷积块注意力模块 (CBAM) 和上下文变压器网络 (CotNet).
  • 应用深度简单在线和实时跟踪 (DeepSORT) 首次在固体废物检测中用于实时识别和跟踪.
  • 开发了MMTrash数据集,这是混合固体废物模拟现实场景的多标签数据集.
  • 集成了改进的你只看一次 (YOLO) 算法与DeepSORT,工业摄像头和PLC控制的机器人臂,用于自动垃圾分类.

主要成果:

  • 与MMTrash和TrashNet数据集上的经典检测算法相比,DS YOLO-Trash表现出优越的性能.
  • 该系统实现了固体废物的实时识别和跟踪.
  • 注意力机制和DeepSORT的整合增强了功能挖掘,并优化了学习过程.

结论:

  • DSYOLO-Trash系统为智能废物管理提供了显著的进步.
  • 拟议的模型和数据集为实时废物分类提供了可靠的解决方案.
  • 这项工作通过改进的废物分类技术,为城市的可持续发展做出了贡献.