A deep learning application for real-time debris detection: underwater environment
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2024
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| Online Access: | http://hdl.handle.net/10361/22752 |
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10361-227522024-05-07T21:02:43Z A deep learning application for real-time debris detection: underwater environment Sinthia, Afsana Kabir Rasel, Annajiat Alim Department of Computer Science and Engineering, Brac University Convolutional neural network Underwater waste classification YOLO Underwater exploration This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (page 41-45). Recognizing the significance of accurately measuring and removing underwater waste is vital for safeguarding marine ecosystems and the environment. Measuring underwater waste is challenging due to factors like light reflection, absorption, dispersed particulates, and color distortion. Detecting and measuring floating and surface waste is comparatively straightforward. The presence of marine waste is detrimental to both the environment and human health, as microplastics from decomposed waste can enter the food chain. In light of current circumstances, addressing water contamination is crucial for environmental preservation. A significant concern in today’s society is the contamination of water bodies. The absence of standardized benchmarks and data standards poses challenges in comparing research efforts related to automatic waste identification in underwater environments. This article tackles the issues of identifying underwater waste or debris by thoroughly examining existing publicly available underwater waste datasets and evaluating Deep Learningbased waste detection algorithms for underwater environments. Image processing, deep learning, and trawling hold promise in implementing effective solutions. Examination of publicly available datasets in this study can support future research efforts to protect our ecosystem. It consolidates prior research, presenting the results of tests conducted on the provided datasets, aiming to establish a reproducible benchmark for waste detection using YOLOv8 as well as classify the garbage using transformers (ViT and Swin) and transfer learning (DenseNet, VGG16 ResNet and InceptionV3). Used ICRA19 dataset encompasses a range of categories of waste, including bio, plastic, and ROV. On the other hand, we used the Forward Looking Sonar Image (FLS) Marine Debris Dataset having 10 Debris categories. The technique of this study achieves a maximum average accuracy which 92.2%, indicating successful waste detection and identification in underwater settings. Afsana Kabir Sinthia B.Sc. in Computer Science and Engineering 2024-05-07T03:51:39Z 2024-05-07T03:51:39Z © 2023. 2023-12 Thesis ID 23166004 http://hdl.handle.net/10361/22752 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. 56 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Convolutional neural network Underwater waste classification YOLO Underwater exploration |
| spellingShingle |
Convolutional neural network Underwater waste classification YOLO Underwater exploration Sinthia, Afsana Kabir A deep learning application for real-time debris detection: underwater environment |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Rasel, Annajiat Alim |
| author_facet |
Rasel, Annajiat Alim Sinthia, Afsana Kabir |
| format |
Thesis |
| author |
Sinthia, Afsana Kabir |
| author_sort |
Sinthia, Afsana Kabir |
| title |
A deep learning application for real-time debris detection: underwater environment |
| title_short |
A deep learning application for real-time debris detection: underwater environment |
| title_full |
A deep learning application for real-time debris detection: underwater environment |
| title_fullStr |
A deep learning application for real-time debris detection: underwater environment |
| title_full_unstemmed |
A deep learning application for real-time debris detection: underwater environment |
| title_sort |
deep learning application for real-time debris detection: underwater environment |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22752 |
| work_keys_str_mv |
AT sinthiaafsanakabir adeeplearningapplicationforrealtimedebrisdetectionunderwaterenvironment AT sinthiaafsanakabir deeplearningapplicationforrealtimedebrisdetectionunderwaterenvironment |
| _version_ |
1814308093107896320 |