Deep Learning and Augmented-Reality Glasses based Meat Cooking Support for Color Vision Disorder Compensation
Deep Learning and Augmented-Reality Glasses based Meat Cooking Support for Color Vision Disorder Compensation
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DOI：
10.1109/nicoint59725.2023.00019
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Shota Chiba;Zhenyang Zhu;D. Inoue;Xiaoyang Mao
中科院分区：
文献类型：
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作者：
Shota Chiba;Zhenyang Zhu;D. Inoue;Xiaoyang Mao
患有色觉障碍(CVD)的人识别颜色有困难，煮肉的能力有限，即无法判断肉是煮得不熟还是煮得过熟。现有研究中提出的大多数CVD补偿方法都集中在CVD个体经历的对比度损失问题上，这些方法不适用于肉类烹饪任务；而一些针对这一任务的研究可能会随着光照条件的变化而失去效果。在这项研究中，我们提出了一个CVD支持系统，该系统利用深度学习模型自动确定烹饪程度，并使用增强现实(AR)眼镜将生成的支持信息呈现给CVD个体。为了使所提出的系统能够执行预测，我们在本研究中创建了一个肉类烹饪数据集。为了验证该方法的有效性，进行了定量和主观评价实验。实验结果表明，用该系统烹调的肉比没有用该系统烹制的肉要好。
People who suffer from color vision disorder (CVD) have difficulties recognizing colors and have limited capability to cook the meat, i.e., could not judge whether the meat is undercooked, or overcooked. Most CVD compensation approaches proposed in the existing studies focused on the problem of contrast loss experienced by CVD individuals, and these methods are not applicable to the meat-cooking task; while some studies proposed for this task may lose effect when the lighting condition changes. In this study, we propose a system for CVD support, which automatically determines the degree of cooking utilizing deep learning model and presents generated support information to CVD individuals using augmented-reality (AR) glasses. To enable the capability of the proposed system to perform the prediction, we create a meat-cooking dataset in this study. To confirm the effectiveness of the proposed method, quantitative and subjective evaluation experiments were conducted. The experimental results showed that the meat cooked with assistance of the proposed is better than those without the system.