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Object Detection for Classifying Sushi Dishes in Conveyor Belt Sushi Business

Published: 20 September 2022 Publication History

Abstract

Over the years, AI has emerged as a key enabler for disruptive innovation. Object detection is an area of deep learning that involves computer vision and image processing to detect various types of objects in images or videos. This paper presents an approach of unified, real-time object detection to implement parts of the billing system of conveyor belt sushi restaurant. Rather than manually counting the number of consumed dishes, the model implemented with YOLOv4 in this work could recognize distinct sushi plates from the image taken by a smartphone camera, and return the class label indicating the item price for further calculation of the dining charge. The model performance achieved Precision 95%, Recall 96%, and mAP 95.45%. The presented model would benefit operational efficiency, support real-time calculation of dining charge, improve service quality, reduce paper and electronic waste.

References

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Yeongjin Oh, Seunghyun Son, and Gyumin Sim. Sushi Dish - Object detection and classification from real images. arXiv.org, 2022. [Online]. Available: https://arxiv.org/abs/1709.00751
[2]
Petru Soviany, and Radu T. Ionescu. Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction. In SYNASC 2018: 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, September 20-23, 2018, Romania. pp. 209-214.
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A Gentle Introduction to Object Recognition with Deep Learning: 2022. from https://machinelearningmastery.com/object-recognition-with-deep-learning/
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MachineLearningMastery,2022.[Online].Available:https://machinelearningmastery.com/object-recognition-with-deep-learning/. [Accessed: 22- Feb- 2022]
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Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. Retrieved Feb 23, 2022 from https://doi.org/10.48550/arXiv.1506.02640
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Jeong-ah Kim, Ju-Yeoung Sung, and Se-ho Park, "Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition”, 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), 2020, pp. 1-4.
[7]
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-yuan Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection", arXiv.org, 2022. from https://arxiv.org/abs/2004.10934v1
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Shailender Kumar, Vishal, Pranav Sharma, and Nitin Pal, "Object tracking and counting in a zone using YOLOv4, DeepSORT and TensorFlow”, 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 1017-1022.
[9]
"GitHub - tzutalin/labelImg: LabelImg is a graphical image annotation tool and label object bounding boxes in images", GitHub, 2022. from https://github.com/tzutalin/labelImg
[10]
"GitHub - AlexeyAB/darknet: YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )", GitHub, 2022. from https://github.com/AlexeyAB/darknet

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ICCTA '22: Proceedings of the 2022 8th International Conference on Computer Technology Applications
May 2022
286 pages
ISBN:9781450396226
DOI:10.1145/3543712
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 20 September 2022

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  1. conveyor belt sushi
  2. object detection
  3. smart billing

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