Classification of Underwater Fish Image Based on Supervised Contrastive Learning

Classification of underwater fish images is of great scientific research significance for assessing the ocean's health. However, compared with land-based optical images, underwater optical images are characterized by color distortion, low contrast, and high noise intensity, making it challenging to perform effective feature extraction and train a highly accurate recognition model for underwater images. This paper proposes a supervised contrastive learning-based classification method for underwater fish images. First, the residual idea is integrated to improve the representation of the last four stages of the residual network to obtain the stage residual network, which more fully utilizes the shallow features and reduces the risk of network overfitting at the same time; second, the channel attention mechanism is incorporated at the end of the last four stages of the backbone network, which prompts the model to pay more attention to the feature channels with high contribution; finally, the contrastive learning loss function is improved by the introduction of labels, and realizes the supervised contrastive learning. Dividing the backbone network and the classifier into two stages of training effectively closes the intra-class distance and distances the inter-class distance and improves the model's performance. The experimental results show that the accuracy, precision, recall, and F1 value of this paper's model on the public benchmark dataset Fish4Knowledge are 99.28%, 98.17%, 94.15%, and 95.71%, and it can realize the classification of underwater fish images under extreme noise conditions more efficiently.