The recognition of fish species holds significant importance in aquaculture and marine biology. However, it is a challenging problem due to the high similarity among intra-genus species. Existing recognition methods primarily seek prominent features of the species. However, we believe that the diverse levels of similarity between a species and other species can also function as implicit characteristics for that specific species. Based on this perspective, we propose a dual-branch fusion network for fine-grained fish species recognition utilizing inter-species similarity. This approach consists of a backbone network and two branches for coarse- and fine-grained recognition. In the coarse-grained branch, we designed a guidance matrix and species similarity labels to facilitate the generation of species similarity information. In the fine-grained branch, features from the backbone network are fused with similarity information to achieve precise recognition. Finally, fine-tuning the neural network through loss functions. We conduct experimental validation on three publicly available fish datasets, yielding excellent accuracy outcomes. Code is available at https://github.com/xingxing317/fish_classification.

鱼类物种的识别在水产养殖和海洋生物学中具有重要意义。然而，由于属内物种之间的高度相似性，这是一个具有挑战性的问题。现有的识别方法主要寻找物种的显着特征。然而，我们相信，一个物种与其他物种之间不同程度的相似性也可以作为该特定物种的隐含特征。基于这个观点，我们提出了一种利用物种间相似性进行细粒度鱼类物种识别的双分支融合网络。该方法由一个主干网络和两个用于粗粒度和细粒度识别的分支组成。在粗粒度分支中，我们设计了指导矩阵和物种相似性标签，以方便生成物种相似性信息。在细粒度分支中，将主干网络的特征与相似性信息融合以实现精确识别。最后，通过损失函数对神经网络进行微调。我们对三个公开的鱼类数据集进行了实验验证，产生了出色的准确性结果。代码可在 https://github.com/xingxing317/fish_classification 获取。