MWaste: A Deep Learning Approach to Manage Household Waste

Suman Kunwar

doi:10.20944/preprints202304.0066.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

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.

Waste Classification, Deep Learning, Waste Management, Computer Vision

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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MWaste: A Deep Learning Approach to Manage Household Waste

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