用YOLO V5识别垃圾种类，提升分类效率。
Optimizing Waste Management with Advanced Object Detection for Garbage Classification
- 基于YOLO V5构建垃圾检测模型，实现多类垃圾精准识别。
- 可识别塑料、纸张、玻璃、金属、纸板和可降解物等六类垃圾。
- 为智能分拣系统与未来垃圾收集机器人提供技术基础。
垃圾产生与乱扔是全球性环境挑战。尽管大规模开展垃圾收集与分类，现有方法仍效率低下，导致回收率不足。为此，开发基于AI的先进系统成为更省力、高效的解决方案。此类模型可用于分拣系统或未来垃圾收集机器人。近年来，对象检测在物体识别方面取得显著进展。本文综述了利用人工智能进行垃圾分类的研究，重点使用YOLO V5进行训练与测试。研究证明，该模型能有效识别包括塑料、纸张、玻璃、金属、纸板及可降解物在内的多种垃圾类型。
原文摘要 · Abstract (English)
Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal. Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively. These models can be applied to sorting systems or possibly waste collection robots that may produced in the future. AI models have grown significantly at identifying objects through object detection. This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing. The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.
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