用深度学习自动识别渔船视频中的鱼，提升海洋资源管理效率
Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries
- 用自定义图像增强和难样本挖掘优化鱼的重识别流程
- Swin-T模型达41.65% mAP@k和90.43% Rank-1准确率，优于ResNet-50
- 适合渔业电子监控、细粒度图像识别研究者参考
精准的渔业数据对可持续海洋资源管理至关重要。随着电子监控（EM）系统的应用，视频数据量激增，已无法人工逐帧审核。本文针对该问题，构建了基于AutoFish数据集的自动化鱼类重识别（Re-ID）深度学习流程，该数据集模拟传送带上的六种外观相似鱼类。通过结合硬三元组挖掘与专用于该数据集的图像归一化处理，显著提升了Re-ID性能（R1和mAP@k）。实验表明，基于视觉变压器的Swin-T架构持续优于基于卷积神经网络的ResNet-50，最高达到41.65% mAP@k和90.43% Rank-1准确率。深入分析发现，同种鱼个体间区分是主要挑战，视角不一致的影响远大于部分遮挡。代码与文档已开源。
原文摘要 · Abstract (English)
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: https://github.com/msamdk/Fish_Re_Identification.git
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