用深度学习实现电子垃圾快速分类，提升回收效率。
A.R.I.S.: Automated Recycling Identification System for E-Waste Classification Using Deep Learning
- 基于YOLOx模型实时识别金属、塑料和电路板。
- 分类精度达90%，mAP为82.2%，分选纯度84%。
- 低成本便携设计，适合推广至基层回收场景。
传统电子废弃物回收因材料分离与识别能力不足，导致资源浪费严重。本文提出A.R.I.S.（Automated Recycling Identification System），一种低成本、便携式电子垃圾破碎物分拣系统。该系统采用YOLOx模型实现实时分类，可精准识别金属、塑料和电路板，兼具低推理延迟与高检测精度。实验结果表明，整体精度达90%，平均精度均值（mAP）为82.2%，分选纯度为84%。通过融合深度学习与现有分选技术，A.R.I.S.显著提升材料回收效率，降低先进回收技术的推广门槛。本研究支持产品生命周期延长、以旧换新及回收计划，助力全供应链环境影响削减。
原文摘要 · Abstract (English)
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.
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