Recently, a retail industry has trended to adopt automatic checkout systems for enhancing customer sales. Typically, an automatic checkout system employs an object detection technique (i.e., image processing) based on a commonly used deep learning model (e.g., Faster R-CNN and YOLO) that has been trained for items. Therefore, it takes much time to relearn the model whenever an item is added to the system. To save the relearning cost, we propose a novel automatic checkout system reducing the relearning cost of an additional item. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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一种减少额外物品信息重新学习成本的新型自动结账系统的提案

最近，零售业倾向于采用自动结账系统来提高客户销售额。通常，自动结账系统采用基于已针对物品进行训练的常用深度学习模型（例如，Faster R-CNN 和 YOLO）的对象检测技术（即图像处理）。因此，每当向系统添加项目时，都需要花费大量时间来重新学习模型。为了节省重新学习成本，我们提出了一种新颖的自动结帐系统，减少了附加项目的重新学习成本。 © 2024 日本电气工程师协会。由 Wiley 期刊有限责任公司出版。