Automatic dry waste classification for recycling purpose
There has been a serious increment in solid waste in the past decades due to rapid urbanization and industrialization. Therefore, it becomes a big issue and challenges which need to have a great concern, as accumulation of solid waste would result in environmental pollution. Recycling is a method wh...
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ALife Robotics Corporation Ltd
2022
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my.um.eprints.432722025-02-10T04:49:14Z http://eprints.um.edu.my/43272/ Automatic dry waste classification for recycling purpose Baharuddin, Muhammad Nuzul Naim Khan, Hassan Mehmood Mokhtar, Norrima Wan Mahiyiddin, Wan Amirul Rajagopal, Heshalini Adam, Tarmizi Jamaluddin, Jafferi TK Electrical engineering. Electronics Nuclear engineering There has been a serious increment in solid waste in the past decades due to rapid urbanization and industrialization. Therefore, it becomes a big issue and challenges which need to have a great concern, as accumulation of solid waste would result in environmental pollution. Recycling is a method which has been prominent in order to deal with the problems, as it is assumed to be economically and environmentally beneficial. It is important to have a wide number of intelligent waste management system and several methods to overcome this challenge. This paper explores the application of image processing techniques in recyclable variety type of dry waste. An automated vision-based recognition system is modelled on image analysis which involves image acquisition, feature extraction, and classification. In this study, an intelligent waste material classification system is proposed to extract 11 features from each dry waste image. There are 4 classifiers, Quadratic Support Vector Machine, Cubic Support Vector Machine, Fine K-Nearest Neighbor and Weighted K-Nearest Neighbor, were used to classify the waste into different type such as bottle, box, crumble, flat, cup, food container and tin. A Cubic Support Vector Machine (C-SVM) classifier led to promising results with accuracy of training and testing, 83.3 and 81.43, respectively. The performance of C-SVM classifier is considerably good which provides consistent performance and faster computation time. Further classification process is improved by utilization of Speeded-Up Robust Features (SURF) method with some limitations such as longer response and computation time. © The 2022 International Conference on Artificial Life and Robotics (ICAROB2022). ALife Robotics Corporation Ltd 2022 Conference or Workshop Item PeerReviewed Baharuddin, Muhammad Nuzul Naim and Khan, Hassan Mehmood and Mokhtar, Norrima and Wan Mahiyiddin, Wan Amirul and Rajagopal, Heshalini and Adam, Tarmizi and Jamaluddin, Jafferi (2022) Automatic dry waste classification for recycling purpose. In: International Conference on Artificial Life and Robotics, ICAROB 2022, 20-23 January 2022, Virtual, Online. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125146100&partnerID=40&md5=55c4ccb42ce0b6d5184d466c3d28df10 |
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TK Electrical engineering. Electronics Nuclear engineering Baharuddin, Muhammad Nuzul Naim Khan, Hassan Mehmood Mokhtar, Norrima Wan Mahiyiddin, Wan Amirul Rajagopal, Heshalini Adam, Tarmizi Jamaluddin, Jafferi Automatic dry waste classification for recycling purpose |
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There has been a serious increment in solid waste in the past decades due to rapid urbanization and industrialization. Therefore, it becomes a big issue and challenges which need to have a great concern, as accumulation of solid waste would result in environmental pollution. Recycling is a method which has been prominent in order to deal with the problems, as it is assumed to be economically and environmentally beneficial. It is important to have a wide number of intelligent waste management system and several methods to overcome this challenge. This paper explores the application of image processing techniques in recyclable variety type of dry waste. An automated vision-based recognition system is modelled on image analysis which involves image acquisition, feature extraction, and classification. In this study, an intelligent waste material classification system is proposed to extract 11 features from each dry waste image. There are 4 classifiers, Quadratic Support Vector Machine, Cubic Support Vector Machine, Fine K-Nearest Neighbor and Weighted K-Nearest Neighbor, were used to classify the waste into different type such as bottle, box, crumble, flat, cup, food container and tin. A Cubic Support Vector Machine (C-SVM) classifier led to promising results with accuracy of training and testing, 83.3 and 81.43, respectively. The performance of C-SVM classifier is considerably good which provides consistent performance and faster computation time. Further classification process is improved by utilization of Speeded-Up Robust Features (SURF) method with some limitations such as longer response and computation time. © The 2022 International Conference on Artificial Life and Robotics (ICAROB2022). |
| format |
Conference or Workshop Item |
| author |
Baharuddin, Muhammad Nuzul Naim Khan, Hassan Mehmood Mokhtar, Norrima Wan Mahiyiddin, Wan Amirul Rajagopal, Heshalini Adam, Tarmizi Jamaluddin, Jafferi |
| author_facet |
Baharuddin, Muhammad Nuzul Naim Khan, Hassan Mehmood Mokhtar, Norrima Wan Mahiyiddin, Wan Amirul Rajagopal, Heshalini Adam, Tarmizi Jamaluddin, Jafferi |
| author_sort |
Baharuddin, Muhammad Nuzul Naim |
| title |
Automatic dry waste classification for recycling purpose |
| title_short |
Automatic dry waste classification for recycling purpose |
| title_full |
Automatic dry waste classification for recycling purpose |
| title_fullStr |
Automatic dry waste classification for recycling purpose |
| title_full_unstemmed |
Automatic dry waste classification for recycling purpose |
| title_sort |
automatic dry waste classification for recycling purpose |
| publisher |
ALife Robotics Corporation Ltd |
| publishDate |
2022 |
| url |
http://eprints.um.edu.my/43272/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125146100&partnerID=40&md5=55c4ccb42ce0b6d5184d466c3d28df10 |
| _version_ |
1825160589501005824 |
| score |
13.239859 |