لخّصلي

Research Article Deep Learning for Plastic Waste Classification System

Janusz Bobulski and Mariusz Kubanek Czestochowa University of Technology, Department of Computer Science, Cze stochowa, Poland Correspondence should be addressed to Janusz Bobulski; [email protected] Received 21 October 2020; Revised 24 March 2021; Accepted 26 April 2021; Published 5 May 2021 Academic Editor: Miin-Shen Yang Copyright (C) 2021 Janusz Bobulski and Mariusz Kubanek.We proposed a technique that can apply in portable devices for waste recognizing which would be helpful in solving urban waste problems.-e main problem considered in this article is creating an automatic plastic waste segregation method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a sorting plant or home by citizens.-is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.One of the opportunities is the use of deep learning and convolutional neural network.

Research Article
Deep Learning for Plastic Waste Classification System

Janusz Bobulski and Mariusz Kubanek
Czestochowa University of Technology, Department of Computer Science, Cze ̨stochowa, Poland
Correspondence should be addressed to Janusz Bobulski; [email protected]
Received 21 October 2020; Revised 24 March 2021; Accepted 26 April 2021; Published 5 May 2021
Academic Editor: Miin-Shen Yang
Copyright © 2021 Janusz Bobulski and Mariusz Kubanek. -is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Plastic waste management is a challenge for the whole world. Manual sorting of garbage is a difficult and expensive process,
which is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. -e
plastic waste may be automatically chosen on a transmission belt for waste removal by using methods of image processing and
artificial intelligence, especially deep learning, to improve the recycling process. Waste segregation techniques and procedures
are applied to major groups of materials such as paper, plastic, metal, and glass. -ough, the biggest challenge is separating
different materials types in a group, for example, sorting different colours of glass or plastics types. -e issue of plastic garbage
is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material).
-erefore, we should look for ways to separate this waste. One of the opportunities is the use of deep learning and convolutional
neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene,
polypropylene, and polystyrene. -e main problem considered in this article is creating an automatic plastic waste segregation
method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a
sorting plant or home by citizens. We proposed a technique that can apply in portable devices for waste recognizing which
would be helpful in solving urban waste problems.