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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Vydáno v:	Applied Computational Intelligence and Soft Computing Ročník 2021; s. 1 - 7
Hlavní autoři:	Bobulski, Janusz, Kubanek, Mariusz
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Hindawi 2021 John Wiley & Sons, Inc Wiley
Témata:	Artificial intelligence Artificial neural networks Cable television broadcasting industry Cameras Classification Consumption Deep learning Economic justification Garbage Image processing Image transmission Lasers Machine learning Materials management Methods Municipal waste management Neural networks Plastics Polyethylene Polyethylene terephthalate Polystyrene resins Portable equipment Principal components analysis Recycling Recycling (Waste, etc.) Software Spectrum analysis United States Waste management United States
ISSN:	1687-9724, 1687-9732
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Kubanek, Mariusz Bobulski, Janusz
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Snippet	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...
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SubjectTerms	Artificial intelligence Artificial neural networks Cable television broadcasting industry Cameras Classification Consumption Deep learning Economic justification Garbage Image processing Image transmission Lasers Machine learning Materials management Methods Municipal waste management Neural networks Plastics Polyethylene Polyethylene terephthalate Polystyrene resins Portable equipment Principal components analysis Recycling Recycling (Waste, etc.) Software Spectrum analysis United States Waste management
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Title	Deep Learning for Plastic Waste Classification System
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