Image Enhancement for Rotation Sushi Plates Detection in Smart Multi-Platform Billing System

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI:10.1109/seai55746.2022.9832218

Patchariya Piya-Aromrat, Y. Limpiyakorn

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引用次数: 1

Abstract

Conveyor belt sushi restaurants have become worldwide popular. The customers are drawn to the low prices and accessibility of the rotation sushi to save their dining time. These small dishes are typically served on different kinds of plates depending on price. Counting the number of each type of plates can then be computed for bill payment. An AI-based approach for sushi plate detection to overcome the time-consuming plate count is introduced in this work. A smart billing system is developed on multi-platform mobile application. The user can take a photo of stacks of consumed dishes as the input image to the YOLOv4 object detector for classifying the different colored plates. This paper focuses on parts of image enhancement as the system can work on iOS and Android systems. The techniques of color transfer with LAB color space and CLAHE are applied for image quality improvement. The experimental results reported the achievement of higher confidence and the successful detection of the objects from out of color space images.

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智能多平台计费系统中旋转寿司盘检测的图像增强

传送带寿司店在全世界都很受欢迎。顾客们被低廉的价格和方便的轮换寿司所吸引，从而节省了他们的用餐时间。根据价格的不同，这些小菜通常装在不同种类的盘子里。计算每种类型的车牌的数量，然后可以计算账单支付。本文介绍了一种基于人工智能的寿司盘子检测方法，克服了费时的盘子计数问题。在多平台移动应用上开发了智能计费系统。用户可以将一叠一叠吃完的盘子拍成照片，作为输入图像输入到YOLOv4对象检测器，用于对不同颜色的盘子进行分类。由于该系统可以在iOS和Android系统上运行，因此本文重点介绍了图像增强部分。利用LAB色彩空间的色彩转移技术和CLAHE技术对图像质量进行了改进。实验结果表明，该方法取得了较高的置信度，并成功地从非彩色空间图像中检测出了目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)

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