Transfer learning and SE-ResNet152 networks-based for small-scale unbalanced fish species identification

Scientific studies on species identification in fish have considerable significance in aquatic ecosystems and quality evaluation. The morphological differences between different fish species are obvious. Machine learning methods use artificial prior knowledge to extract fish features, which is time-consuming, laborious, and subjective. Recently, deep learning-based identification of fish species has been widely used. However, fish species identification still faces many challenges due to the small scale of fish samples and the imbalance of the number of categories. For example, the model is prone to being overfitted, and the performance of the classifier is biased to the fish species of most samples. To solve the above problems, this paper proposes a fish species identification approach based on SE-ResNet152 and class-balanced focal loss. First, visualization analysis and image preprocessing of fish datasets are carried out. Second, the SE-ResNet152 model is constructed as a generalized feature extractor and is migrated to the target dataset. Finally, we apply the class-balanced focal loss function to train the SE-ResNet152 model, and realize fish species identification on three fish image views (body, head, and scale). The proposed method was tested on the Fish-Pak public dataset and achieved 98.80%, 96.67%, and 91.25% accuracy on the three fish image views, respectively. To ensure the superior performance of the proposed method, we performed an experimental comparison with other methods involving SENet154, DenseNet121, ResNet18, ResNet152, VGG16, cross-entropy, and focal loss. Comprehensive empirical analyses reveal that the proposed method achieves good performance on the three fish image views and outperforms common methods.