Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries

• URL: http://arxiv.org/abs/2512.08400v2
• Date: Thu, 11 Dec 2025 11:39:09 GMT
• Title: Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries
• Authors: Samitha Nuwan Thilakarathna, Ercan Avsar, Martin Mathias Nielsen, Malte Pedersen,
• Abstract summary: We develop an optimized deep learning pipeline for automated fish re-identification using the novel AutoFish dataset.<n>We demonstrate that the Vision Transformer-based Swin-T architecture consistently outperforms the Convolutional Neural Network-based ResNet-50.<n>An in-depth analysis reveals that the primary challenge is distinguishing visually similar individuals of the same species.
• Score: 4.007351600492542
• License: http://creativecommons.org/licenses/by/4.0/
• 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: https://github.com/msamdk/Fish_Re_Identification.git

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