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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my-usm-ep.606612024-05-23T02:47:32Z Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network 2023-09 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 http://eprints.usm.my/60661/ http://eprints.usm.my/60661/1/NAWAF%20FARHAN%20FANKUR%20ALSHDAIFAT%20-%20TESIS24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer |
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Universiti Sains Malaysia |
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USM Institutional Repository |
| language |
English |
| topic |
QA75.5-76.95 Electronic computers Computer science |
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QA75.5-76.95 Electronic computers Computer science Alshdaifat, Nawaf Farhan Fankur Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network |
| 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. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Alshdaifat, Nawaf Farhan Fankur |
| author_facet |
Alshdaifat, Nawaf Farhan Fankur |
| author_sort |
Alshdaifat, Nawaf Farhan Fankur |
| title |
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_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_sort |
multi-fish detection and tracking using track-mask region
convolutional neural network |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Sains Komputer |
| publishDate |
2023 |
| url |
http://eprints.usm.my/60661/1/NAWAF%20FARHAN%20FANKUR%20ALSHDAIFAT%20-%20TESIS24.pdf |
| _version_ |
1804888980430258176 |