Deep Learning for Plastic Waste Classification System
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. The plastic waste may be automatically chosen on a tr...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/6626948 |
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| _version_ | 1832552612537303040 |
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| author | Janusz Bobulski Mariusz Kubanek |
| author_facet | Janusz Bobulski Mariusz Kubanek |
| author_sort | Janusz Bobulski |
| collection | DOAJ |
| description | 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. The 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. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, 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. The 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. |
| format | Article |
| id | doaj-art-fd03804d207942d9ba966ab3c619e757 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-fd03804d207942d9ba966ab3c619e7572025-02-03T05:58:22ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/66269486626948Deep Learning for Plastic Waste Classification SystemJanusz Bobulski0Mariusz Kubanek1Czestochowa University of Technology, Department of Computer Science, Częstochowa, PolandCzestochowa University of Technology, Department of Computer Science, Częstochowa, PolandPlastic 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. The 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. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, 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. The 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.http://dx.doi.org/10.1155/2021/6626948 |
| spellingShingle | Janusz Bobulski Mariusz Kubanek Deep Learning for Plastic Waste Classification System Applied Computational Intelligence and Soft Computing |
| title | Deep Learning for Plastic Waste Classification System |
| title_full | Deep Learning for Plastic Waste Classification System |
| title_fullStr | Deep Learning for Plastic Waste Classification System |
| title_full_unstemmed | Deep Learning for Plastic Waste Classification System |
| title_short | Deep Learning for Plastic Waste Classification System |
| title_sort | deep learning for plastic waste classification system |
| url | http://dx.doi.org/10.1155/2021/6626948 |
| work_keys_str_mv | AT januszbobulski deeplearningforplasticwasteclassificationsystem AT mariuszkubanek deeplearningforplasticwasteclassificationsystem |