A.R.I.S.: Automated Recycling Identification System for E-Waste Classification Using Deep Learning
AI 摘要
A.R.I.S. 通过深度学习 YOLOx 模型，实现了高效的电子垃圾自动分类和回收。
主要贡献
- 提出了基于 YOLOx 的电子垃圾自动分类系统 A.R.I.S.
- 实现了金属、塑料、电路板的实时分类
- 提高了电子垃圾的材料回收效率
方法论
使用 YOLOx 模型进行目标检测和分类，集成到低成本的电子垃圾分拣系统中，通过实验评估性能。
原文摘要
Traditional electronic recycling processes suffer from significant resource loss due to inadequate material separation and identification capabilities, limiting material recovery. We present A.R.I.S. (Automated Recycling Identification System), a low-cost, portable sorter for shredded e-waste that addresses this efficiency gap. The system employs a YOLOx model to classify metals, plastics, and circuit boards in real time, achieving low inference latency with high detection accuracy. Experimental evaluation yielded 90% overall precision, 82.2% mean average precision (mAP), and 84% sortation purity. By integrating deep learning with established sorting methods, A.R.I.S. enhances material recovery efficiency and lowers barriers to advanced recycling adoption. This work complements broader initiatives in extending product life cycles, supporting trade-in and recycling programs, and reducing environmental impact across the supply chain.