A Method for Waste Segregation using Convolutional Neural Networks
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Abstract (arXiv)
Segregation of garbage is a primary concern in many nations across the world. Even though we are in the modern era, many people still do not know how to distinguish between organic and recyclable waste. It is because of this that the world is facing a major crisis of waste disposal. In this paper, we try to use deep learning algorithms to help solve this problem of waste classification. The waste is classified into two categories like organic and recyclable. Our proposed model achieves an accuracy of 94.9%. Although the other two models also show promising results, the Proposed Model stands out with the greatest accuracy. With the help of deep learning, one of the greatest obstacles to efficient waste management can finally be removed.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 23 Feb 2022.
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References · 4
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2020WasteNet: Waste Classification at the Edge for Smart Binscited by 5
- 2021Towards artificially intelligent recycling Improving image processing for waste classificationcited by 4
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