Aquarium Family Fish Species Identification System Using Deep Neural Networks

Khalifa, N. E. M., M. H. N. Taha, and A. E. Hassanien, "Aquarium Family Fish Species Identification System Using Deep Neural Networks", Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, Cham, Springer International Publishing, pp. 347–356, 2019.


In this paper, a system for aquarium family fish species identification is proposed. It identifies eight family fish species along with 191 sub-species. The proposed system is built using deep convolutional neural networks (CNN). It consists of four layers, two convolutional and two fully connected layers. A comparative result is presented against other CNN architectures such as AlexNet and VggNet according to four parameters (number of convolution and fully connected layers, the number of epochs in training phase to achieve 100{%} accuracy, validation accuracy, and testing accuracy). Through the paper, it is proven that the proposed system has competitive results against the other architectures. It achieved 85.59{%} testing accuracy while AlexNet achieves 85.41{%} over untrained benchmark dataset. Moreover, the proposed system has less trained images, less memory, less computational complexity in training, validation, and testing phases.