用局部图像块提升三文鱼重识别准确率，解决标签不全和跨摄像头识别难题。
Patch Ensembles for Robust Salmon Re-Identification with Weak Trajectory Labels
- 基于鱼体侧线定位提取纹理锚定图像块，融合多块预测结果做身份判断
- 跨摄像头测试下mAP从0.609提升至0.860，显著增强泛化能力
- 适用于渔业监控中缺乏完整标注的弱监督场景
商业网箱中三文鱼重识别因种群规模大而困难，严格精度要求导致大规模人工标注不可行。轨迹ID可作为代理标签，但会引入轨迹偏见。为此，我们提出一种基于图像块的重识别框架，通过融合块级预测实现个体身份判定。关键在于预测鱼的侧线位置，从而提取纹理锚定图像块及切片。为实现真实评估，设计多摄像头实验（相机间距6米），使同一条鱼可在不同轨迹中被记录，并通过人工确认构建跨摄像头测试集。该集成方法在同轨迹验证中mAP从0.932提升至0.965，在跨摄像头测试中从0.609提升至0.860，表明其更强的鲁棒性与泛化能力。代码与数据：https://github.com/espenbh/salmon-reid-patch-ensemble。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。