A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples

Simple Summary To address the challenge of limited sample size in fish data, we improved the prototypical networks to enhance model accuracy. In this study, an attention module was introduced on the basis of the prototypical networks, and improvements were made in the calculation of similarity. Across various fish datasets, our model achieved an accuracy improvement of 2% to 10% compared to the prototypical networks. This research has practical application value for scarce marine fish resources.Abstract Fish species identification plays a vital role in marine fisheries resource exploration, yet datasets related to marine fish resources are scarce. In open-water environments, various fish species often exhibit similar appearances and sizes. To solve these issues, we propose a few-shot learning approach to identifying fish species. Our approach involves two key components. Firstly, the embedding module was designed to address the challenges posed by a large number of fish species with similar phenotypes by utilizing the distribution relationships of species in the embedding space. Secondly, a metric function was introduced, effectively enhancing the performance of fish species classification and successfully addressing the issue of limited sample quantity. The proposed model is trained end to end on fish species public datasets including the Croatian fish dataset, Fish4Knowledge and WildFish. Compared with the prototypical networks, our method performs more effectively and improves accuracy by 2% to 10%; it is able to identify fish effectively in small samples sizes and complex scene scenarios. This method provides a valuable technological tool for the development of fisheries resources and the preservation of fish biodiversity.