Using Deep Learning to Detect Polyethylene Terephthalate (PET) Bottle Status for Recycling

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Hellen Wanini Mwangi
Hellen Wanini Mwangi
2
Mpai Mokoena
Mpai Mokoena

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Using Deep Learning to Detect Polyethylene Terephthalate (PET) Bottle Status for Recycling Banner
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Following the recent ban of plastic waste import by China, many developed countries face challenges with their huge amount of plastic waste. Some of the countries have diverted their waste to other developing East-Asia countries like Philippines, Vietnam and Malaysia. However, Malaysian government has taken strict action to send back over 3000 tons of plastic waste to its origin citing contamination. The aim of this paper is to establish mechanisms to detect the status of post-consumer PET bottles for recycling. By encouraging recycling of clean pet bottles, we ensure high quality bottles for recycling. A research based as well as experimental design approach was adopted to develop mechanisms to detect PET bottle status. During the experiment, various PET bottles were collected and images captured. A total of 1749 images were captured using raspberry Pi camera.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Hellen Wanini Mwangi. 2019. \u201cUsing Deep Learning to Detect Polyethylene Terephthalate (PET) Bottle Status for Recycling\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 19 (GJCST Volume 19 Issue G4): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
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GJCST-G Classification: I.2.6
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v1.2

Issue date

November 18, 2019

Language

English

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Following the recent ban of plastic waste import by China, many developed countries face challenges with their huge amount of plastic waste. Some of the countries have diverted their waste to other developing East-Asia countries like Philippines, Vietnam and Malaysia. However, Malaysian government has taken strict action to send back over 3000 tons of plastic waste to its origin citing contamination. The aim of this paper is to establish mechanisms to detect the status of post-consumer PET bottles for recycling. By encouraging recycling of clean pet bottles, we ensure high quality bottles for recycling. A research based as well as experimental design approach was adopted to develop mechanisms to detect PET bottle status. During the experiment, various PET bottles were collected and images captured. A total of 1749 images were captured using raspberry Pi camera.

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Using Deep Learning to Detect Polyethylene Terephthalate (PET) Bottle Status for Recycling

Hellen Wanini Mwangi
Hellen Wanini Mwangi
Mpai Mokoena
Mpai Mokoena

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