Object detection in intelligent billing system for conveyor belt sushi restaurant

Abstract

Organization must automate wherever and whenever they can, particularly during today’s global changes in daily lifestyles. Trends regarding the use of technology, especially AI has emerged as a key enabler for disruptive innovation. This thesis thus presents the application programming interface of object detector implemented with YOLOv4 and OpenCV for classifying the prices of sushi plates distinguished by colors. The object detector is part of the smart cross-platform mobile application to facilitate billing process for conveyor belt sushi business. The frontend is developed with Flutter to build single codebase for UIs. To handle the variants of image colors resulting from the use of different mobile cameras, color transfer is used for transferring the image dataset colors to images captured by users. Microservices architecture is adopted for the backend. Orchestration of YOLOv4, OpenCV and Spring Boot REST API will create APIs to calculate food cost, generate QR code for bill payment, and maintain customer membership benefits. The constructed object detection model achieved the precision of 97%, recall of 97%, F1-score of 97% and mAP of 97.3% The smart billing system presented in this work would accelerate the workflow, increase productivity, reduce waste and drive moving for contactless society.