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[2602.17642] A.R.I.S.: Automated Recycling Identification System for E-Waste Classification Using Deep Learning
Summary
The A.R.I.S. system utilizes deep learning to enhance e-waste recycling by accurately classifying materials in real-time, improving recovery efficiency.
Why It Matters
As e-waste continues to grow, effective recycling methods are crucial for resource recovery and environmental sustainability. A.R.I.S. addresses inefficiencies in traditional recycling processes, potentially transforming the industry and supporting broader sustainability initiatives.
Key Takeaways
- A.R.I.S. employs a YOLOx model for real-time material classification.
- The system achieved 90% overall precision and 84% sortation purity.
- Integrating deep learning with existing methods enhances recycling efficiency.
- A.R.I.S. lowers barriers for advanced recycling adoption.
- Supports environmental sustainability by improving material recovery.
Computer Science > Machine Learning arXiv:2602.17642 (cs) [Submitted on 19 Feb 2026] Title:A.R.I.S.: Automated Recycling Identification System for E-Waste Classification Using Deep Learning Authors:Dhruv Talwar, Harsh Desai, Wendong Yin, Goutam Mohanty, Rafael Reveles View a PDF of the paper titled A.R.I.S.: Automated Recycling Identification System for E-Waste Classification Using Deep Learning, by Dhruv Talwar and 4 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.17642 [cs.LG] (or ar...