A Proposal of a Novel Automatic Checkout System Reducing Additional Item Information Relearning Cost

Abstract

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.