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.

翻译：传统电子回收工艺因材料分离与识别能力不足，导致显著的资源损耗，限制了材料回收率。本文提出A.R.I.S.（自动回收识别系统），一种面向破碎电子废弃物的低成本便携分选装置，以解决此效率瓶颈。该系统采用YOLOx模型对金属、塑料及电路板进行实时分类，在实现高检测精度的同时保持低推理延迟。实验评估显示整体精确率达90%，平均精度均值（mAP）为82.2%，分选纯度为84%。通过将深度学习技术与成熟分选方法相结合，A.R.I.S.提升了材料回收效率，并降低了先进回收技术的应用门槛。本工作可延伸至延长产品生命周期、支持以旧换新与回收计划、降低供应链环境影响的更广泛倡议中形成技术补充。