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DOI: 10.15862/04INOR224 (https://doi.org/10.15862/04INOR224)

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Prokashev V.A. Opportunities for improving the use of computer vision in municipal solid waste sorting. Russian journal of resources, conservation and recycling. 2,24; 11(2). Available at: https://resources.today/PDF/04INOR224.pdf (in Russian). DOI: 10.15862/04INOR224

Opportunities for improving the use of computer vision in municipal solid waste sorting

Prokashev Vitalii Andreyevich
National University of Science and Technology «MISiS», Moscow, Russia
E-mail: vitalyaprocashev@gmail.com; vitalya-procashev@yandex.ru
ORCID: https://orcid.org/0009-0005-6793-7257

Abstract. This article explores the possibilities of improving the application of optical sorting of municipal solid waste on conveyor belts using computer vision and machine learning technologies in order to replace manual sorting lines. Also the relevance of improving waste sorting is emphasized. This work expands the understanding of the application of computer vision in the field of waste management and offers practical recommendations for its implementation in industrial processes.

The paper reviews current approaches to sorting municipal solid waste, analyzes their effectiveness, and explores the potential for improving waste management processes and increasing recycling efficiency based on recent research in this area. The most popular equipment used in sorting and its features are considered.

A generalization of the morphological composition of waste is carried out, and the minimum and maximum possible shares of each class of waste in the recycling stream are identified. Based on this data, sufficient proportions of images in each category are selected to train a convolutional neural network for waste classification.

Applied research in terms of detection and classification of waste objects on conveyor belts using well-known neural network architectures is considered. The analysis showed that most solutions are based on 2018 architectures, while the reference classification accuracy of more modern architectures, such as OmniVec, is 17 % higher, and detection efficiency is similar orders of magnitude higher. The resulting classification accuracy is significantly affected by image augmentation in the set of images for training the neural network — its presence can increase accuracy by 10 to 15 %. The minimum training data set has been determined, which should be about 2 000 images due to the fact that after a further increase in the number, the accuracy does not change significantly.

Keywords: municipal solid waste; computer vision; convolutional neural networks; object detection; classification; sorting; morphological composition

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