Fish species recognition using transfer learning techniques

Abstract

Marine species recognition is the process of identifying various species that help in population estimation and identifying the endangered types for taking further remedies and actions. The superior performance of deep learning for classification is due to the property of estimating millions of parameters that have to be extracted from many annotated datasets. However, many types of fish species are becoming extinct, which may reduce the number of samples. The unavailability of a large dataset is a significant hurdle for applying a deep neural network that can be overcome using transfer learning techniques. To overcome this problem, we propose a transfer learning technique using a pre-trained model that uses underwater fish images as input and applies a transfer learning technique to detect the fish species using a pre-trained Google Inception-v3 model. We have evaluated our proposed method on the Fish4knowledge(F4K) dataset and obtained an accuracy of 95.37%. The research would be helpful to identify fish existence and quantity for marine biologists to understand the underwater environment to encourage its preservation and study the behavior and interactions of marine animals.