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arXiv · 2605.18038

Patch Ensembles for Robust Salmon Re-Identification with Weak Trajectory Labels

Espen Uri Høgstedt·Christian Schellewald·Annette Stahl·Rudolf Mester
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Abstract

Salmon re-identification in commercial net-pens is challenging due to large populations, which impose strict accuracy requirements and make large-scale labeled data acquisition infeasible. Trajectory IDs can be used as proxy labels, but this introduces trajectory-ID bias. To address these challenges, we propose a patch-based re-identification framework that fuses patch-level predictions into a salmon identity decision. A key component is the prediction of the salmon's lateral line, enabling extraction of texture-anchored patches and patch slices. To enable realistic evaluation, we introduce an experimental setup using multiple cameras placed 6 m apart, allowing the same fish to be recorded in different trajectories. This enables the construction of a cross-camera test set through manual match confirmation. Our ensemble approach outperforms the full-image baseline in same-trajectory validation (0.932 to 0.965 mAP) and cross-camera testing (0.609 to 0.860 mAP). The substantial improvements in the cross-camera setting demonstrate improved generalizability and robustness. Code and data: https://github.com/espenbh/salmon-reid-patch-ensemble.

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Espen Uri Høgstedt, Christian Schellewald, Annette Stahl, Rudolf Mester. 2026-05-18. Patch Ensembles for Robust Salmon Re-Identification with Weak Trajectory Labels. https://arxiv.org/abs/2605.18038

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