Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network

Alshdaifat, Nawaf Farhan Fankur (2023) Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network. PhD thesis, Universiti Sains Malaysia.

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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.

Item Type: Thesis (PhD)
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: Mr Noor Azizan Abu Hashim
Date Deposited: 23 May 2024 02:47
Last Modified: 23 May 2024 02:47

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