实时智能垃圾监测和高效收集使用Yolov8和Yolov5深度学习模型来实现环境可持续性
Mohammed M Abo-Zahhad1, Mohammed Abo-Zahhad2,3
1Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, New Sohag City, Egypt.
Scientific reports
|May 9, 2025
概括
这项研究引入了一个用于智能废物管理的AI系统,使用YOLOv5和YOLOv8等深度学习模型来检测溢出的垃圾桶并有效地分类垃圾. 该系统提高了垃圾收集的准确性,并降低了计算成本,以更好地利用资源.
科学领域:
- 环境科学与工程环境科学与工程
- 计算机科学和人工智能 人工智能
- 智慧城市技术 智慧城市技术
背景情况:
- 预计到2050年,全球废物产量将达到34亿,这将给环境带来重大挑战,尤其是在快速城市化地区.
- 当前的废物管理系统往往缺乏实时监控,导致效率低下,因垃圾桶溢出而造成环境污染.
- 人们越来越需要智能系统来优化垃圾收集和改善城市清洁.
研究的目的:
- 为有效的废物管理开发和评估一个增强的实时物体检测系统.
- 实施轻量级深度学习模型 (YOLOv5,YOLOv8) 以实现有效的垃圾分类和垃圾桶监控.
- 为了降低智能废物管理解决方案的计算成本和硬件要求.
主要方法:
- 利用采用YOLOv5和YOLOv8用于垃圾检测和分类的两阶段轻量级深度学习方法.
- 开发了一个系统,在垃圾桶上安装了低成本设备,以测量填充水位和检测外部垃圾.
- 在公共和自定义垃圾数据集上比较了YOLOv8,YOLOv5和EfficientNet模型的性能.
主要成果:
- 拟议的系统准确地检测到溢出的垃圾桶,容器外的垃圾袋,并将废物分类为主要类别 (满,袋,外,湿).
- YOLOv5和YOLOv8在垃圾检测和分类方面表现出高精度,YOLOv5在非GPU硬件上显示出有效性.
- YOLOv8和YOLOv5模型在对大多数垃圾类别进行分类时,表现优于EfficientNet,特别是"垃圾桶满".
结论:
- 开发的系统为实时废物监测和管理提供了可靠和高效的解决方案,解决了以前系统的局限性.
- 轻量级的深度学习模型显著降低了计算需求,使智能废物管理更容易获得和更具成本效益.
- 对目标封闭,硬件优化和机器人集成的进一步研究可以进一步提高垃圾收集效率.
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