In conveyor belt sushi restaurants, billing is a burdened job because one has to manually count the number of dishes and identify the color of them to calculate the price. In a busy situation, there can be a mistake that customers are overcharged or under-charged. To deal with this problem, we developed a method that automatically identifies the color of dishes and calculate the total price using real images. Our method consists of ellipse fitting and convol-utional neural network. It achieves ellipse detection precision 85% and recall 96% and classification accuracy 92%.
Cite
@article{arxiv.1709.00751,
title = {Sushi Dish - Object detection and classification from real images},
author = {Yeongjin Oh and Seunghyun Son and Gyumin Sim},
journal= {arXiv preprint arXiv:1709.00751},
year = {2017}
}