Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries
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
6941306f65f1e24a11780751
Samitha Nuwan Thilakarathna
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2025-12-16T10:18:13.036Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/0tA-73JLuKIdbpuW_NLyZ.png
samitha Thilakarathna
samDK
user
samDK
6941306f65f1e24a11780752
Ercan Avsar
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2025-12-16T14:10:34.082Z
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Ercan
ercaaqua
user
ercaaqua
6941306f65f1e24a11780753
Martin Mathias Nielsen
6941306f65f1e24a11780754
Malte Pedersen
Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries
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
The study presents an optimized deep learning pipeline using the AutoFish dataset and Swin-T architecture to improve fish re-identification metrics in electronic monitoring systems.
fish re-identification Re-ID hard triplet mining image transformation pipeline dataset-specific normalization Vision Transformer Swin-T Convolutional Neural Network ResNet-50 intra-species errors viewpoint inconsistency partial occlusion