Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran

Recycle waste is an integral part of our daily lives. It generates various type of waste materials in our homes, workplaces and communities. With a growing population and urbanization, it is crucial to prioritize responsible waste management practices to address the environmental challenges faced by...

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Main Author:	Amran, Aiman Syafwan
Format:	Thesis
Language:	English
Published:	2023
Subjects:	TD Environmental technology Sanitary engineering
Online Access:	https://ir.uitm.edu.my/id/eprint/88976/1/88976.pdf
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id	my-uitm-ir.88976
record_format	uketd_dc
spelling	my-uitm-ir.889762024-03-19T07:07:20Z Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran 2023 Amran, Aiman Syafwan TD Environmental technology. Sanitary engineering Recycle waste is an integral part of our daily lives. It generates various type of waste materials in our homes, workplaces and communities. With a growing population and urbanization, it is crucial to prioritize responsible waste management practices to address the environmental challenges faced by the country. In Malaysia, the traditional approach to recycle waste detection and classification primarily relies on manual sorting and visual inspection by waste management personnel. When recyclable waste arrives at recycling centers or facilities, workers manually separate the materials based on their visual appearance and physical characteristics. Because of that, this project aims to detect and classify a typed of recycled waste such paper, plastic and metal. It uses YOLOv5 object detection and classification algorithm. This project uses the images of paper, plastic and metal gathered from Kaggle and GitHub dataset. This system was put to two tests of testing which were functionality testing of the whole system and the metric evaluation of the object detection and classification model. The object detection and classification algorithm achieved 91.9% mean average precision in metric evaluation. The system was developed as a web-based system in order to make it easily accessible by the target user which the governance body from any public nor private sectors. The recommendation on the future work is to improve the detection model for it to be able to detect small size object from the image to make the system more reliable. 2023 Thesis https://ir.uitm.edu.my/id/eprint/88976/ https://ir.uitm.edu.my/id/eprint/88976/1/88976.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	TD Environmental technology Sanitary engineering
spellingShingle	TD Environmental technology Sanitary engineering Amran, Aiman Syafwan Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
description	Recycle waste is an integral part of our daily lives. It generates various type of waste materials in our homes, workplaces and communities. With a growing population and urbanization, it is crucial to prioritize responsible waste management practices to address the environmental challenges faced by the country. In Malaysia, the traditional approach to recycle waste detection and classification primarily relies on manual sorting and visual inspection by waste management personnel. When recyclable waste arrives at recycling centers or facilities, workers manually separate the materials based on their visual appearance and physical characteristics. Because of that, this project aims to detect and classify a typed of recycled waste such paper, plastic and metal. It uses YOLOv5 object detection and classification algorithm. This project uses the images of paper, plastic and metal gathered from Kaggle and GitHub dataset. This system was put to two tests of testing which were functionality testing of the whole system and the metric evaluation of the object detection and classification model. The object detection and classification algorithm achieved 91.9% mean average precision in metric evaluation. The system was developed as a web-based system in order to make it easily accessible by the target user which the governance body from any public nor private sectors. The recommendation on the future work is to improve the detection model for it to be able to detect small size object from the image to make the system more reliable.
format	Thesis
qualification_level	Bachelor degree
author	Amran, Aiman Syafwan
author_facet	Amran, Aiman Syafwan
author_sort	Amran, Aiman Syafwan
title	Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
title_short	Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
title_full	Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
title_fullStr	Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
title_full_unstemmed	Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran
title_sort	real-time intelligent recycle waste detection and classification using you only look once version 5 / aiman syafwan amran
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/88976/1/88976.pdf
_version_	1794192181973483520

Real-time intelligent recycle waste detection and classification using you only look once version 5 / Aiman Syafwan Amran

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