Abstract
<abstract><p>Computer vision methods are effective in classifying garbage into recycling categories for waste processing but 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.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
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