Image recognition approach for expediting chinese cafeteria checkout process

doi:10.1109/LifeTech48969.2020.1570616877

機構典藏 > College of Global Entrepreneurial Development > Department of Innovative Information and Technology > Proceeding > Item 987654321/118718

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118718

Title:	Image recognition approach for expediting chinese cafeteria checkout process
Authors:	Wu, B.-T.;Tsou, Y.-W.;Tan, E.;Chang, F.-C.
Keywords:	food recognition;automatic price calculation;nutrition facts calculation;object detection;YOLOv3;image recognition;machine learning;transfer learning
Date:	2020-04-30
Issue Date:	2020-06-01 12:15:25 (UTC+8)
Abstract:	One of the common running themes in modern-day Chinese cafeterias is the hold up in foot traffic in queueing due to checkout. We find out that this bottleneck is caused by the staff requiring extra time to look up the prices of those miscellaneous entrees and calculating the total due amount during checkout. In this paper, this issue is addressed by introducing real-time image recognition techniques into this process. By using a webcam taking live video feed at the checkout desk with the image recognition model outputs the total due amount simultaneously, we are able to eliminate the need to perform manual price calculations. Additionally, the nutrition facts of the meal can also be calculated and displayed to the customers based on the detected entrees. The image recognition model is based on YOLOv3 with 575 entree-catered plate images involved in model training, validation, and testing. The transfer learning technique is also incorporated to speed up the training process. Experimental results show that the recognition accuracy of individual entree is around 70% and that of the entire plate is roughly 63%. With the advanced training with a larger dataset, we believe that the accuracy can be increased, and applying the approach during the checkout will become more practicable.
DOI:	10.1109/LifeTech48969.2020.1570616877
Appears in Collections:	[Department of Innovative Information and Technology] Proceeding

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