Date of Award
12-2025
Degree Type
Masters Thesis
Degree Name
Master of Science (MS)
School
Computing Sciences and Computer Engineering
Committee Chair
Dr. Chaoyang Zhang
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Dr. Sarah Lee
Committee Member 2 School
Computing Sciences and Computer Engineering
Committee Member 3
Dr. Zhaoxian Zhou
Committee Member 3 School
Computing Sciences and Computer Engineering
Abstract
This study investigates automated deep-learning methodologies for two primary tasks, firstly fish and its habitat classification and deep-sea fish detection using the DeepFish dataset. A pretrained ResNet-50 model attained a validation accuracy of 82.8% for multi-class habitat classification, demonstrating robust performance in most habitats based on 6,517 images, although exhibiting lower recall for visually similar or underrepresented classes. A binary ResNet-50 classifier achieved 99.8% accuracy in distinguishing fish from. no-fish images using approximately 40,000 images. The YOLOv5s model, trained on 4,505 images containing 15,463 bounding-box annotations, achieved a mean average precision (mAP@0.5) of 98.2% All models were trained on the SeaHawk computing cluster with four GPUs. These findings highlight promising directions for future research in video-based modeling, semi-supervised learning and efficient marine edge computing.
Copyright
Poojitha Priyadarshini Madari, 2025
Recommended Citation
Madari, Poojitha Priyadarshini, "A Study on Deepsea Fish Detection Using Convolutional Neural Networks" (2025). Master's Theses. 1150.
https://aquila.usm.edu/masters_theses/1150