An efficient approach for recyclable waste detection and classification using image processing techniques
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2024
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| Accés en línia: | http://hdl.handle.net/10361/22092 |
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10361-220922024-01-10T21:02:40Z An efficient approach for recyclable waste detection and classification using image processing techniques Chowdhury, Prabal Kumar Islam, Md. Aminul Haque, Md Aminul Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University TrashNet Deep learning Object detection Image classification CNN VGG16 Inception-Resnet-v2 MobileNet YOLOv5 YOLOv7 Neural network Image processing Waste products. Image processing. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). One of the world’s most pressing issues right now is the lack of a competent waste management system, particularly in emerging and underdeveloped countries. Re cycling solid waste, which comprises numerous dangerous non-biodegradable sub stances like glass, metals, plastics, etc., is the most essential step in reducing waste related issues in the environment. Typically, collected waste includes all types of waste that must be thoroughly sorted to recycle efficiently. Most countries use man ual waste sorting techniques, which are efficient. Nevertheless, the waste sorting process by human being is not safe as there is always a risk of exposing them selves to toxic wastes, which could be serious for their health. Our thesis presents a Deep Learning technique based on computer vision for automatically identifying waste. To construct the model, we used Convolutional Neural Networks, real-time object detection systems, such as YOLOv5 and YOLOv7, as well as several trans fer learning-based architectures, including VGG16, MobileNet, Inception-Resnet-v2. The model is trained on numerous images for each type of waste to ensure no overfit ting and greater accuracy. The highest accuracy we achieved for our waste detection model YOLOv5x is 93.7%. Prabal Kumar Chowdhury Md. Aminul Islam Md Aminul Haque B.Sc. in Computer Science 2024-01-10T03:29:36Z 2024-01-10T03:29:36Z 2023 2023-01 Thesis ID: 22241150 ID: 19101398 ID: 19101580 http://hdl.handle.net/10361/22092 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. 38 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
TrashNet Deep learning Object detection Image classification CNN VGG16 Inception-Resnet-v2 MobileNet YOLOv5 YOLOv7 Neural network Image processing Waste products. Image processing. |
| spellingShingle |
TrashNet Deep learning Object detection Image classification CNN VGG16 Inception-Resnet-v2 MobileNet YOLOv5 YOLOv7 Neural network Image processing Waste products. Image processing. Chowdhury, Prabal Kumar Islam, Md. Aminul Haque, Md Aminul An efficient approach for recyclable waste detection and classification using image processing techniques |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Chowdhury, Prabal Kumar Islam, Md. Aminul Haque, Md Aminul |
| format |
Thesis |
| author |
Chowdhury, Prabal Kumar Islam, Md. Aminul Haque, Md Aminul |
| author_sort |
Chowdhury, Prabal Kumar |
| title |
An efficient approach for recyclable waste detection and classification using image processing techniques |
| title_short |
An efficient approach for recyclable waste detection and classification using image processing techniques |
| title_full |
An efficient approach for recyclable waste detection and classification using image processing techniques |
| title_fullStr |
An efficient approach for recyclable waste detection and classification using image processing techniques |
| title_full_unstemmed |
An efficient approach for recyclable waste detection and classification using image processing techniques |
| title_sort |
efficient approach for recyclable waste detection and classification using image processing techniques |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22092 |
| work_keys_str_mv |
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| _version_ |
1814307153201070080 |