Transfer Learning with deep Convolutional Neural Network for Underwater Live Fish Recognition

Recently, underwater video analysis are more used by marine ecologists to study fish populations as this technique is non-destructively, generates huge amount of visual data and does not perturb underwater environment. Automated methods for processing the recorded data are required because visual processing can be time consuming, subjective and costly. However, the underwater environment poses great challenges due to changes in luminosity, complex backgrounds and free movement of fish. In this paper, we present a convolutional neural network that was trained with transfer learning framework for fish species recognition. First, we use the original AlexNet model to extract fish features from images on the available underwater dataset. Then, to improve the performance, we fine-tune the model on the dataset. Finally, we re-extract features after that AlexNet has been fine-tuned. We use a linear SVM classifier for species classification. The proposed approach reach an accuracy of more than 99% that demonstrates its effectiveness.