Abstract
Visual food recognition systems deployed in real-world environments, such as automated conveyor-belt inspection, are highly sensitive to domain shifts caused by illumination changes. While recent studies have shown that lighting variations can significantly distort food perception by both humans and AI, existing works are often limited to single food categories or controlled settings, and most public food datasets lack explicit illumination annotations. In this work, we investigate illumination-induced domain shift in multi-class food category recognition using two widely adopted datasets, Food-101 and Fruits-360. We demonstrate substantial accuracy degradation under cross-dataset evaluation due to mismatched visual conditions. To address this challenge, we construct synthetic illumination-augmented datasets by systematically varying light temperature and intensity, enabling controlled robustness analysis without additional labels. We further evaluate cross-dataset transfer learning and domain generalization, with a focus on illumination-sensitive target categories such as apple-based classes. Experimental results show that illumination-aware augmentation significantly improves recognition robustness under domain shift while preserving real-time performance. Our findings highlight the importance of illumination robustness and provide practical insights for deploying reliable food recognition systems in real-world inspection scenarios.
Abstract (translated)
在现实世界环境中部署的视觉食品识别系统（例如自动化传送带检查）对由于照明变化引起的领域偏移非常敏感。最近的研究表明，光照的变化会显著扭曲人类和AI对食物的感知。然而，现有的大多数研究通常仅限于单一的食物类别或受控设置，并且大多数公开的食物数据集缺乏明确的照明标注。 在这项工作中，我们使用两个广泛采用的数据集（Food-101 和 Fruits-360）来调查多类食品分类识别中的光照引起的领域偏移问题。我们在跨数据集评估中展示了由于视觉条件不匹配导致的显著准确性下降。为了解决这一挑战，我们通过系统地改变光源温度和强度构建了合成光照增强的数据集，这使我们能够在无需额外标注的情况下进行受控鲁棒性分析。此外，我们还评估了跨数据集迁移学习和领域泛化，重点关注对光照敏感的目标类别（如苹果类）。实验结果表明，在保留实时性能的同时，光照感知增强显著提高了在域偏移下的识别鲁棒性。 我们的研究强调了照明鲁棒性的关键重要性，并为部署可靠的食物识别系统于现实世界中的检查场景提供了实用见解。
URL
https://arxiv.org/abs/2602.08491
