Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

MWaste: A Deep Learning Approach to Manage Household Waste

Version 1 : Received: 4 April 2023 / Approved: 5 April 2023 / Online: 5 April 2023 (15:25:05 CEST)

A peer-reviewed article of this Preprint also exists.

Kunwar, S. (2023). MWaste: An app that uses deep learning to manage household waste. Clean Technologies and Recycling, 3(3), 119-133. Kunwar, S. (2023). MWaste: An app that uses deep learning to manage household waste. Clean Technologies and Recycling, 3(3), 119-133.

Abstract

Computer vision methods have shown to be effective in classifying garbage into recycling categories for waste processing, existing methods are costly, imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.

Keywords

Waste Classification, Deep Learning, Waste Management, Computer Vision

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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