Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network
Deep learning has become more common in recent years due to its excellent results in many areas. This thesis primarily focuses on multi-fish detection and tracking methods in underwater videos. The existing multi-fish detection methods for underwater videos have a low detection rate and consumes...
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Online Access: | http://eprints.usm.my/60661/ http://eprints.usm.my/60661/1/NAWAF%20FARHAN%20FANKUR%20ALSHDAIFAT%20-%20TESIS24.pdf |
| _version_ | 1848884506557153280 |
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| author | Alshdaifat, Nawaf Farhan Fankur |
| author_facet | Alshdaifat, Nawaf Farhan Fankur |
| author_sort | Alshdaifat, Nawaf Farhan Fankur |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Deep learning has become more common in recent years due to its excellent results
in many areas. This thesis primarily focuses on multi-fish detection and tracking methods
in underwater videos. The existing multi-fish detection methods for underwater
videos have a low detection rate and consumes time in the training and testing process
due to the underwater conditions and the overfitting during training. Many multi-fish
detection and tracking methods for underwater videos (based on deep learning) where
low accuracy for multi-fish tracking and occlusion instances during multi-fish tracking
leads to inability to distinguish edges, and inability to handle each detected object over
time. Therefore, this research aims to improve and enhance methods for multi-fish
detection and tracking in underwater videos based on the latest deep learning algorithms.
The proposed improved multi-fish detection method involves three main steps:
1) Improving ResNet-101 backbone for better fish detection, 2) Enhancing the Region
Proposal Network (RPN) method based on Faster R-CNN for multi-fish detection and
3) An improved multi-fish detection method in terms of accuracy and with a lower
training and testing times by utilising the aforementioned methods. The proposed
multi-fish tracking method (Track-Mask R-CNN) also exhibits similar enhanced characteristics
compared to the state-of-art methods (using fish dataset). An accuracy of
86.7% and 78.9% have been achieved for the proposed multi-fish detection and tracking
respectively. |
| first_indexed | 2025-11-15T19:07:47Z |
| format | Thesis |
| id | usm-60661 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:07:47Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-606612024-05-23T02:47:32Z http://eprints.usm.my/60661/ Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network Alshdaifat, Nawaf Farhan Fankur QA75.5-76.95 Electronic computers. Computer science Deep learning has become more common in recent years due to its excellent results in many areas. This thesis primarily focuses on multi-fish detection and tracking methods in underwater videos. The existing multi-fish detection methods for underwater videos have a low detection rate and consumes time in the training and testing process due to the underwater conditions and the overfitting during training. Many multi-fish detection and tracking methods for underwater videos (based on deep learning) where low accuracy for multi-fish tracking and occlusion instances during multi-fish tracking leads to inability to distinguish edges, and inability to handle each detected object over time. Therefore, this research aims to improve and enhance methods for multi-fish detection and tracking in underwater videos based on the latest deep learning algorithms. The proposed improved multi-fish detection method involves three main steps: 1) Improving ResNet-101 backbone for better fish detection, 2) Enhancing the Region Proposal Network (RPN) method based on Faster R-CNN for multi-fish detection and 3) An improved multi-fish detection method in terms of accuracy and with a lower training and testing times by utilising the aforementioned methods. The proposed multi-fish tracking method (Track-Mask R-CNN) also exhibits similar enhanced characteristics compared to the state-of-art methods (using fish dataset). An accuracy of 86.7% and 78.9% have been achieved for the proposed multi-fish detection and tracking respectively. 2023-09 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/60661/1/NAWAF%20FARHAN%20FANKUR%20ALSHDAIFAT%20-%20TESIS24.pdf Alshdaifat, Nawaf Farhan Fankur (2023) Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Alshdaifat, Nawaf Farhan Fankur Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network |
| title | Multi-fish Detection And Tracking Using Track-mask Region
Convolutional Neural Network |
| title_full | Multi-fish Detection And Tracking Using Track-mask Region
Convolutional Neural Network |
| title_fullStr | Multi-fish Detection And Tracking Using Track-mask Region
Convolutional Neural Network |
| title_full_unstemmed | Multi-fish Detection And Tracking Using Track-mask Region
Convolutional Neural Network |
| title_short | Multi-fish Detection And Tracking Using Track-mask Region
Convolutional Neural Network |
| title_sort | multi-fish detection and tracking using track-mask region
convolutional neural network |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/60661/ http://eprints.usm.my/60661/1/NAWAF%20FARHAN%20FANKUR%20ALSHDAIFAT%20-%20TESIS24.pdf |