Sea fish identification using convolutional neural network

Abstract. In recent years, international organizations have regulated fishery in public seas to conserve marine ecosystems. Automatically identifying the types of fish catch is one of the most impartial approaches for monitoring fishing practices. Hence, this study proposes the use of image processing and deep learning algorithms to automatically identify deep-sea fish. Two convolutional neural network classifiers were developed to differentiate fish into four classes: tuna, billfish, shark, and others. During this process, two pre-trained models were employed as the base network and then fine-tuned to improve the identification ability when facing fine-grained-image classification problems. The experimental results show that these four classes of fish can be identified with a relatively high degree of accuracy (94.5% for the VGG16-based classifier and 92.25% for the Inception-V3-based classifier).