Date of Award
Master of Science in Computer Science
School of Computer Science and Engineering
First Reader/Committee Chair
The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of 100% over the Large-scale fish dataset, with the potential for real-time deployment in aquatic monitoring systems. Furthermore, this work proposes avenues for future research in the domain of fish classification.
Adapa, Priyanka, "CLASSIFICATION OF LARGE SCALE FISH DATASET BY DEEP NEURAL NETWORKS" (2023). Electronic Theses, Projects, and Dissertations. 1820.