Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant
Nowadays, a crucial issue in major cities throughout the world is waste management where tons of waste being generated every single day. Fortunately, people can count on other methods to protect the environment through waste recycling. In most countries, waste that can be recycled are being categori...
| Main Authors: | , , , , , |
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| Format: | Article |
| Jezik: | English |
| Izdano: |
Blue Eyes Intelligence Engineering & Sciences Publication
2019
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| Online dostop: | http://psasir.upm.edu.my/id/eprint/80774/1/WASTE.pdf |
| _version_ | 1825951340266782720 |
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| author | Mustaffa, Mas Rina Nasharuddin, Nurul Amelina Hussin, Masnida Mohd Nazri, Nur Izzahtul Nabilah Zakaria, Alya Hidayah Nik Ahmad Zamri, Nik Nur Ellya Arisha |
| author_facet | Mustaffa, Mas Rina Nasharuddin, Nurul Amelina Hussin, Masnida Mohd Nazri, Nur Izzahtul Nabilah Zakaria, Alya Hidayah Nik Ahmad Zamri, Nik Nur Ellya Arisha |
| author_sort | Mustaffa, Mas Rina |
| collection | UPM |
| description | Nowadays, a crucial issue in major cities throughout the world is waste management where tons of waste being generated every single day. Fortunately, people can count on other methods to protect the environment through waste recycling. In most countries, waste that can be recycled are being categorised or handled manually by using human labour. The objective of this project is to develop an automated recyclable waste classification method which can replace the traditional ways of dealing with three types of waste, namely plastic bottles, papers, and soda cans. Firstly, we computed a global threshold value based on the Otsu method to obtain a binary image representation. Few morphological operators are then executed to obtain the regions of interest (waste’s object). For feature representation, we calculated multiple shape properties of the waste’s object such as perimeter, area, eccentricity, and major axis length. We experimented the extracted feature vectors with few classifiers. Our findings have shown that the waste
classification prototype is able to effectively categorise waste up
to 94.4% accuracy based on the proposed shape representation
and Quadratic Discriminant classifier. |
| first_indexed | 2024-03-06T10:28:32Z |
| format | Article |
| id | upm.eprints-80774 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:28:32Z |
| publishDate | 2019 |
| publisher | Blue Eyes Intelligence Engineering & Sciences Publication |
| record_format | dspace |
| spelling | upm.eprints-807742020-10-15T22:25:55Z http://psasir.upm.edu.my/id/eprint/80774/ Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant Mustaffa, Mas Rina Nasharuddin, Nurul Amelina Hussin, Masnida Mohd Nazri, Nur Izzahtul Nabilah Zakaria, Alya Hidayah Nik Ahmad Zamri, Nik Nur Ellya Arisha Nowadays, a crucial issue in major cities throughout the world is waste management where tons of waste being generated every single day. Fortunately, people can count on other methods to protect the environment through waste recycling. In most countries, waste that can be recycled are being categorised or handled manually by using human labour. The objective of this project is to develop an automated recyclable waste classification method which can replace the traditional ways of dealing with three types of waste, namely plastic bottles, papers, and soda cans. Firstly, we computed a global threshold value based on the Otsu method to obtain a binary image representation. Few morphological operators are then executed to obtain the regions of interest (waste’s object). For feature representation, we calculated multiple shape properties of the waste’s object such as perimeter, area, eccentricity, and major axis length. We experimented the extracted feature vectors with few classifiers. Our findings have shown that the waste classification prototype is able to effectively categorise waste up to 94.4% accuracy based on the proposed shape representation and Quadratic Discriminant classifier. Blue Eyes Intelligence Engineering & Sciences Publication 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80774/1/WASTE.pdf Mustaffa, Mas Rina and Nasharuddin, Nurul Amelina and Hussin, Masnida and Mohd Nazri, Nur Izzahtul Nabilah and Zakaria, Alya Hidayah and Nik Ahmad Zamri, Nik Nur Ellya Arisha (2019) Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant. International Journal of Innovative Technology and Exploring Engineering, 8 (8S). pp. 270-274. ISSN 2278-3075 https://www.ijitee.org/wp-content/uploads/papers/v8i8s/H10450688S19.pdf |
| spellingShingle | Mustaffa, Mas Rina Nasharuddin, Nurul Amelina Hussin, Masnida Mohd Nazri, Nur Izzahtul Nabilah Zakaria, Alya Hidayah Nik Ahmad Zamri, Nik Nur Ellya Arisha Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant |
| title | Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant |
| title_full | Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant |
| title_fullStr | Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant |
| title_full_unstemmed | Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant |
| title_short | Automated recyclable waste classification using multiple shape-based properties and quadratic discriminant |
| title_sort | automated recyclable waste classification using multiple shape based properties and quadratic discriminant |
| url | http://psasir.upm.edu.my/id/eprint/80774/1/WASTE.pdf |
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