对基于人工智能的自动化废物分类技术进行系统审查
Farnaz Fotovvatikhah1, Ismail Ahmedy1, Rafidah Md Noor1
1Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
Sensors (Basel, Switzerland)
|May 28, 2025
概括
人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 提供废物分类的自动化解决方案. 本综述分析了人工智能技术和数据集,强调深度学习和混合模型对未来的废物管理系统具有前景.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的废物分类是劳动密集型和低效的.
- 自动化废物分类对于有效的废物管理至关重要.
- 机器学习 (ML) 和深度学习 (DL) 提供了可行的计算替代方案.
研究的目的:
- 系统地审查人工智能 (AI),ML和DL在废物分类自动化中的应用.
- 分析现有的废物分类数据集并确定其局限性.
- 提出未来人工智能驱动废物分类研究和开发的路线图.
主要方法:
- 根据PRISMA和基彻纳姆指南进行了系统文献审查 (SLR).
- 分析了97多项基于人工智能的废物分类研究.
- 将人工智能技术分类为基于ML,基于DL和混合模型.
- 审查了15个公开可用的废物分类数据集.
主要成果:
- 深度学习和混合人工智能模型在当前的废物分类研究中占主导地位.
- 卷积神经网络 (CNN) 架构和转移学习显示出显著的前景.
- 关键数据集的局限性包括不平衡,现实世界的可变性和缺乏标准化.
- 人工智能技术为高效和自动化废物管理提供了一条道路.
结论:
- 人工智能,特别是DL和混合方法,正在改变废物分类.
- 解决数据集的局限性和专注于实际部署是未来进步的关键.
- 一个结构化的路线图优先考虑了人工智能在废物管理中的挑战和机遇,平衡了准确性,效率和可持续性.
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