Patch Ensembles for Robust Salmon Re-Identification with Weak Trajectory Labels
For aquaculture researchers needing robust fish re-identification under weak labels, this method offers a practical solution with significant accuracy gains.
Salmon re-identification in commercial net-pens is challenging due to large populations and infeasible large-scale labeled data acquisition. The proposed patch-based ensemble framework with lateral line prediction improves same-trajectory mAP from 0.932 to 0.965 and cross-camera mAP from 0.609 to 0.860.
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.