Deep learning-based waste classification system for efficient waste management

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Detalles Bibliográficos
Main Authors:	Nakib, Abdullah Al, Talukder, Md. Nayem, Majumder, Chinmoy, Biswas, Soptorshi, Hassan, Jabid
Outros autores:	Alam, Md. Golam Rabiul
Formato:	Thesis
Idioma:	English
Publicado:	Brac University 2022
Subjects:	CNN Mask R-CNN ResNet-101 Grad-CAM Deep learning Waste classification Cognitive learning theory (Deep learning) Artificial intelligence Machine learning
Acceso en liña:	http://hdl.handle.net/10361/16096

id	10361-16096
record_format	dspace
spelling	10361-160962022-02-06T21:01:30Z Deep learning-based waste classification system for efficient waste management Nakib, Abdullah Al Talukder, Md. Nayem Majumder, Chinmoy Biswas, Soptorshi Hassan, Jabid Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University CNN Mask R-CNN ResNet-101 Grad-CAM Deep learning Waste classification Cognitive learning theory (Deep learning) Artificial intelligence Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-30). A smart waste management system plays a vital role in building cleanliness, hygienic, and healthier living for the inhabitants of a city. However, the inherent problems of the waste management system are still a matter of great concern even amid this cutting edge of science and technologies. The root cause of this problem points to one fact - which is too much manual labor in the garbage collection, separation, and recycling process. In this research, we have used the Deep Learning-based model ‘Mask R-CNN’ to detect and classify Kitchen Waste, Glass Waste, Metal Waste, Paper Waste, and Plastic Waste from garbage dump waste images for the automation of the waste management system. We have also used the Explainable AI algorithm ‘Grad-CAM’ to introduce explainability to our model which helped to identify the most important features of each object and understand decisions of Mask R-CNN. Mask R-CNN model achieved 92.58% accuracy in classifying the 5 waste categories. Abdullah Al Nakib Md. Nayem Talukder Chinmoy Majumder Soptorshi Biswas Jabid Hassan B. Computer Science 2022-02-06T05:03:43Z 2022-02-06T05:03:43Z 2021 2021-10 Thesis ID 17101145 ID 17201026 ID 18201108 ID 17301073 ID 17201056 http://hdl.handle.net/10361/16096 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 30 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	CNN Mask R-CNN ResNet-101 Grad-CAM Deep learning Waste classification Cognitive learning theory (Deep learning) Artificial intelligence Machine learning
spellingShingle	CNN Mask R-CNN ResNet-101 Grad-CAM Deep learning Waste classification Cognitive learning theory (Deep learning) Artificial intelligence Machine learning Nakib, Abdullah Al Talukder, Md. Nayem Majumder, Chinmoy Biswas, Soptorshi Hassan, Jabid Deep learning-based waste classification system for efficient waste management
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Nakib, Abdullah Al Talukder, Md. Nayem Majumder, Chinmoy Biswas, Soptorshi Hassan, Jabid
format	Thesis
author	Nakib, Abdullah Al Talukder, Md. Nayem Majumder, Chinmoy Biswas, Soptorshi Hassan, Jabid
author_sort	Nakib, Abdullah Al
title	Deep learning-based waste classification system for efficient waste management
title_short	Deep learning-based waste classification system for efficient waste management
title_full	Deep learning-based waste classification system for efficient waste management
title_fullStr	Deep learning-based waste classification system for efficient waste management
title_full_unstemmed	Deep learning-based waste classification system for efficient waste management
title_sort	deep learning-based waste classification system for efficient waste management
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/16096
work_keys_str_mv	AT nakibabdullahal deeplearningbasedwasteclassificationsystemforefficientwastemanagement AT talukdermdnayem deeplearningbasedwasteclassificationsystemforefficientwastemanagement AT majumderchinmoy deeplearningbasedwasteclassificationsystemforefficientwastemanagement AT biswassoptorshi deeplearningbasedwasteclassificationsystemforefficientwastemanagement AT hassanjabid deeplearningbasedwasteclassificationsystemforefficientwastemanagement
_version_	1814308620642287616

Deep learning-based waste classification system for efficient waste management

Títulos similares