EcoBin: A Two-Stage Deep Convolutional Neural Network for Contamination-Aware Waste Classification
arXiv:2606.15547v1 Announce Type: cross Abstract: Waste classification models have become highly accurate at sorting waste, often exceeding 95% on benchmark datasets. However, these models fail to account for contamination in recyclable waste. We present EcoBin, a two-stage deep convolutional neural network that classifies household waste by its disposal pathway and that explicitly accounts for contamination. The first stage is a base waste classifier built on an EfficientNetV2-S backbone that assigns each of the thirty waste categories in our dataset to one of four disposal pathways. The seco
The increasing sophistication of deep learning and the growing global imperative for efficient waste management and recycling are converging to create solutions for real-world environmental problems.
This development addresses a critical barrier in waste recycling by explicitly accounting for contamination, which significantly improves the viability and efficiency of automated waste sorting.
Current waste classification models now have a path to account for contamination, moving beyond simple material identification to practical disposal pathway assignment, making automated recycling more effective.
- · Waste management industry
- · Recycling companies
- · Environmental tech startups
- · Urban populations
- · Landfill operators (long term)
- · Inefficient manual sorting processes
Improved accuracy and efficiency in automated waste sorting, reducing contamination rates in recyclable streams.
Increased adoption of AI-powered waste management systems, leading to higher recycling rates and reduced landfill waste.
New regulatory frameworks encouraging contamination-aware AI in waste processing, creating niche markets for specialized AI hardware and software.
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Read at arXiv cs.AI