用合成数据提升老化物体重识别准确率，最高增13%。
Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis
- 用合成数据训练模型，模拟材料老化过程
- 结合动态画廊更新，平均Rank-1准确率提升24%
- 开源2696张木托盘老化图像数据集，适合长时重识别研究
本文研究了合成训练数据在重识别任务中对材料磨损与老化预测的影响。通过不同实验设置和画廊扩展策略，分析了随时间推移的老化重识别性能变化。采用持续更新的画廊，逐步考虑材料老化后，平均Rank-1准确率提升了24%。此外，使用仅10%人工数据训练的模型相比纯真实数据训练模型，Rank-1准确率最高提升13%，显著增强了对测试数据的泛化能力。最后，本文提出一个新型开源重识别数据集pallet-block-2696，包含2696张欧洲木托盘在4个月内拍摄的图像，期间发生自然老化及使用损坏，外观变化明显，可用于生成合成老化木制材料样本。
原文摘要 · Abstract (English)
This contribution explores the impact of synthetic training data usage and the prediction of material wear and aging in the context of re-identification. Different experimental setups and gallery set expanding strategies are tested, analyzing their impact on performance over time for aging re-identification subjects. Using a continuously updating gallery, we were able to increase our mean Rank-1 accuracy by 24%, as material aging was taken into account step by step. In addition, using models trained with 10% artificial training data, Rank-1 accuracy could be increased by up to 13%, in comparison to a model trained on only real-world data, significantly boosting generalized performance on hold-out data. Finally, this work introduces a novel, open-source re-identification dataset, pallet-block-2696. This dataset contains 2,696 images of Euro pallets, taken over a period of 4 months. During this time, natural aging processes occurred and some of the pallets were damaged during their usage. These wear and tear processes significantly changed the appearance of the pallets, providing a dataset that can be used to generate synthetically aged pallets or other wooden materials.
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