Publication: Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network
| dc.contributor.author | Alshdaifat, Nawaf Farhan Fankur | |
| dc.date.accessioned | 2024-05-23T02:50:53Z | |
| dc.date.available | 2024-05-23T02:50:53Z | |
| dc.date.issued | 2023-09 | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/19318 | |
| dc.language.iso | en_US | |
| dc.subject | Multi-fish Detection | |
| dc.subject | Tracking Using Track-mask Region Convolutional Neural Network | |
| dc.subject | mask Region Convolutional Neural Network | |
| dc.subject | Alshdaifat | |
| dc.subject | Nawaf Farhan Fankur | |
| dc.subject | Pusat Pengajian Sains Komputer | |
| dc.title | Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |