Abstract
Introduction Fish re-identification (re-ID) is of great significance for fish monitoring and can contribute to aquaculture and fish breeding. Synchronizing information from different cameras is beneficial for optimizing re-ID performance. Methods We constructed the first underwater fish re-identification benchmark dataset (FS48) under three camera conditions. FS48 encompasses 48 different fish identities, 10,300 frames, and 39,088 bounding boxes, covering various lighting conditions and background environments. Additionally, we developed the first robust and accurate fish re-identification baseline, FSNet, which fuses information from three camera positions by extracting features from synchronized video frames of each position and combining the synchronized information. Results The experimental results show that FS48 is universal and of high quality. FSNet has an effective network design and demonstrates good performance, achieving better re-identification performance by combining information from three positions, helping improve overall re-test accuracy, and evaluating the effectiveness of re-identification among detectors. Discussion Our dataset will be released upon acceptance of this paper, which is expected to further promote the development of underwater fish re-identification.