dc.contributor.advisor | Alam, Md. Golam Rabiul | |
dc.contributor.author | Nakib, Abdullah Al | |
dc.contributor.author | Talukder, Md. Nayem | |
dc.contributor.author | Majumder, Chinmoy | |
dc.contributor.author | Biswas, Soptorshi | |
dc.contributor.author | Hassan, Jabid | |
dc.date.accessioned | 2022-02-06T05:03:43Z | |
dc.date.available | 2022-02-06T05:03:43Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021-10 | |
dc.identifier.other | ID 17101145 | |
dc.identifier.other | ID 17201026 | |
dc.identifier.other | ID 18201108 | |
dc.identifier.other | ID 17301073 | |
dc.identifier.other | ID 17201056 | |
dc.identifier.uri | http://hdl.handle.net/10361/16096 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 29-30). | |
dc.description.abstract | 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. | en_US |
dc.description.statementofresponsibility | Abdullah Al Nakib | |
dc.description.statementofresponsibility | Md. Nayem Talukder | |
dc.description.statementofresponsibility | Chinmoy Majumder | |
dc.description.statementofresponsibility | Soptorshi Biswas | |
dc.description.statementofresponsibility | Jabid Hassan | |
dc.format.extent | 30 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | 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. | |
dc.subject | CNN | en_US |
dc.subject | Mask R-CNN | en_US |
dc.subject | ResNet-101 | en_US |
dc.subject | Grad-CAM | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Waste classification | en_US |
dc.subject.lcsh | Cognitive learning theory (Deep learning) | |
dc.subject.lcsh | Artificial intelligence | |
dc.subject.lcsh | Machine learning | |
dc.title | Deep learning-based waste classification system for efficient waste management | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |