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