Abstract
Accurate fisheries data are crucial for effective and sustainable marine resource management. With the recent adoption of Electronic Monitoring (EM) systems, more video data is now being collected than can be feasibly reviewed manually. This paper addresses this challenge by developing an optimized deep learning pipeline for automated fish re-identification (Re-ID) using the novel AutoFish dataset, which simulates EM systems with conveyor belts with six similarly looking fish species. We demonstrate that key Re-ID metrics (R1 and mAP@k) are substantially improved by using hard triplet mining in conjunction with a custom image transformation pipeline that includes dataset-specific normalization. By employing these strategies, we demonstrate that the Vision Transformer-based Swin-T architecture consistently outperforms the Convolutional Neural Network-based ResNet-50, achieving peak performance of 41.65% mAP@k and 90.43% Rank-1 accuracy. An in-depth analysis reveals that the primary challenge is distinguishing visually similar individuals of the same species (Intra-species errors), where viewpoint inconsistency proves significantly more detrimental than partial occlusion. The source code and documentation are available at: this https URL
Abstract (translated)
精确的渔业数据对于有效和可持续的海洋资源管理至关重要。随着最近采用电子监控（EM）系统的广泛应用，现在收集到的视频数据量已超出人工审查的能力范围。本文通过开发一种优化的深度学习流水线来应对这一挑战，该管道用于使用新型AutoFish数据集自动识别鱼类，该数据集模拟了具有六种外观相似鱼种传送带的电子监控系统。我们展示了通过结合硬三元组挖掘和定制的图像转换流程（包括特定于数据集的标准化）可以显著提高关键再识别指标（R1 和 mAP@k）。通过采用这些策略，基于视觉变压器的Swin-T架构在实验中始终优于基于卷积神经网络的ResNet-50，达到了41.65% 的mAP@k和90.43% 的Rank-1精度。深入分析表明，主要挑战在于区分同一物种中的外观相似个体（种内错误），其中视角不一致比部分遮挡更具破坏性。源代码和文档可在以下网址获得：[此URL]
URL
https://arxiv.org/abs/2512.08400
