Keywords: environmental machine learning, sustainablity computing, automated recycling, adaptive waste management, environmental health systems, climate-aware recycling, machine learning for circular economy
TL;DR: A deep learning system that simultaneously sorts waste and collects data for industrial plastic recycling. It combines real-time classification using the largest dataset (40K samples) to achieve 200 samples/hour across 5 recyclable plastic types."
Abstract: Efficient sorting of plastic waste remains a critical bottleneck in recycling systems, with current approaches relying on manual labor or semi-automated solutions that contribute to large amounts of plastics ending up in landfills. Despite the rapid growth of the global plastic recycling market, projected to reach $120 billion by 2030, existing sorting technologies struggle to meet demands for accuracy and
throughput [14]. While recent deep learning breakthroughs show promise in waste sorting, a complete industrial-scale pipeline has been overlooked. We propose a novel, low-cost deep learning system that addresses real-world challenges in plastic sorting such as varying material types, inconsistent lighting conditions, and contaminated surfaces. Our key contributions include: (1) a scalable deep
learning architecture featuring two adaptive pipelines - one for data collection and another for classification, optimized for industrial deployment, (2) curation of the world’s first comprehensive industrial dataset of 40,000 plastic samples, and (3) an interpretable approach leveraging Grad-CAM and t-SNE visualizations to tackle challenging cases like dark and distorted plastics. The proposed sorting system
demonstrates commercial viability by processing 200 samples per hour in five plastic types common in municipal solid waste (MSW), with a potential cost of $30 per ton.
Submission Number: 19
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