Enhanced Fish Species Detection and Classification Using a Novel Deep Learning Approach

This study presents an innovative deep learning approach for accurate fish species detection and classification in underwater environments. We introduce FishNet, a novel convolutional neural network architecture that combines attention mechanisms, transfer learning, and data augmentation techniques to improve fish recognition in challenging aquatic conditions. Our method was evaluated on the Fish4Knowledge dataset, achieving a mean average precision (mAP) of 92.3% for detection and 89.7%accuracy for species classification, outperforming existing state-of-the-art models. The proposed approach demonstrates robust performance across various underwater conditions, including different lighting, turbidity, and occlusion scenarios, making it suitable for real-world applications in marine biology, fisheries management, and ecological monitoring.

@article{Iqtait2024,
  title     = {Enhanced Fish Species Detection and Classification Using a Novel Deep Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Musab Iqtait and Marwan Harb Alqaryouti and Ala Eddin Sadeq and Ahmad Aburomman and Mahmoud Baniata and Zaid Mustafa and Huah Yong Chan},
  doi       = {10.14569/IJACSA.2024.01510108},
  url       = {https://doi.org/10.14569/IJACSA.2024.01510108}
}