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Smart cashierless checkout system for retail using machine vision

As Corona Virus 2019 (COVID-19) pandemic strikes the world, retail industry has been severely impacted especially in its daily operation due to the restriction of workforce and most of the face-to-face services, including checkout, are associated with high risk of developing spread chain of COVID-19...

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Main Author: Lee, Ren Yi
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utar.edu.my/5393/1/MH_1702805_Final_%2D_REN_YI_LEE.pdf
http://eprints.utar.edu.my/5393/
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spelling my-utar-eprints.53932023-06-16T14:28:34Z Smart cashierless checkout system for retail using machine vision Lee, Ren Yi TJ Mechanical engineering and machinery As Corona Virus 2019 (COVID-19) pandemic strikes the world, retail industry has been severely impacted especially in its daily operation due to the restriction of workforce and most of the face-to-face services, including checkout, are associated with high risk of developing spread chain of COVID-19 virus. Despite there are multiple computer vision-based solutions available in the field such as on-shelf checkout and sensor fusion, but they can be expensive and may require overhaul of stores, which is unfeasible for small retail stores. Therefore, a software prototype of intelligent cashierless checkout system is proposed to help small-scale retail stores in minimizing the risk of developing COVID-19 virus spread chain as well as the workforce requirement during checkout using state-of-the-art object detection models. This project was performed in 2 parts where the first stage involved an image synthesis algorithm to automatically produce visually realistic product images using Generative Adversarial Network (GAN). Several GAN architectures such as CycleGAN and AttentionGAN were studied and compared in terms of their effectiveness in generating realistic shadow in actual checkout scenario. CycleGAN results in more realistic shadow with Fréchet inception distance (FID) of 40.99. In the following stage, a publicly available dataset, MVTec D2S dataset were used to benchmark multiple object detection models used for product recognition. By using You Only Look Once (YOLO) v5L as the baseline model, several improved models were developed by replacing the backbone structure with other light-weight architectures to improve computation efficiency when deployed on edge devices. After training the model with dataset generated in previous stage, the proposed model with MobileNet V3 surpassed baseline model in terms of inference time, with only 0.142s while maintaining high Mean Average Precision (mAP) of 98.2% and Checkout Accuracy (cAcc) of 89.17% on Jetson Nano. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5393/1/MH_1702805_Final_%2D_REN_YI_LEE.pdf Lee, Ren Yi (2022) Smart cashierless checkout system for retail using machine vision. Final Year Project, UTAR. http://eprints.utar.edu.my/5393/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Lee, Ren Yi
Smart cashierless checkout system for retail using machine vision
description As Corona Virus 2019 (COVID-19) pandemic strikes the world, retail industry has been severely impacted especially in its daily operation due to the restriction of workforce and most of the face-to-face services, including checkout, are associated with high risk of developing spread chain of COVID-19 virus. Despite there are multiple computer vision-based solutions available in the field such as on-shelf checkout and sensor fusion, but they can be expensive and may require overhaul of stores, which is unfeasible for small retail stores. Therefore, a software prototype of intelligent cashierless checkout system is proposed to help small-scale retail stores in minimizing the risk of developing COVID-19 virus spread chain as well as the workforce requirement during checkout using state-of-the-art object detection models. This project was performed in 2 parts where the first stage involved an image synthesis algorithm to automatically produce visually realistic product images using Generative Adversarial Network (GAN). Several GAN architectures such as CycleGAN and AttentionGAN were studied and compared in terms of their effectiveness in generating realistic shadow in actual checkout scenario. CycleGAN results in more realistic shadow with Fréchet inception distance (FID) of 40.99. In the following stage, a publicly available dataset, MVTec D2S dataset were used to benchmark multiple object detection models used for product recognition. By using You Only Look Once (YOLO) v5L as the baseline model, several improved models were developed by replacing the backbone structure with other light-weight architectures to improve computation efficiency when deployed on edge devices. After training the model with dataset generated in previous stage, the proposed model with MobileNet V3 surpassed baseline model in terms of inference time, with only 0.142s while maintaining high Mean Average Precision (mAP) of 98.2% and Checkout Accuracy (cAcc) of 89.17% on Jetson Nano.
format Final Year Project / Dissertation / Thesis
author Lee, Ren Yi
author_facet Lee, Ren Yi
author_sort Lee, Ren Yi
title Smart cashierless checkout system for retail using machine vision
title_short Smart cashierless checkout system for retail using machine vision
title_full Smart cashierless checkout system for retail using machine vision
title_fullStr Smart cashierless checkout system for retail using machine vision
title_full_unstemmed Smart cashierless checkout system for retail using machine vision
title_sort smart cashierless checkout system for retail using machine vision
publishDate 2022
url http://eprints.utar.edu.my/5393/1/MH_1702805_Final_%2D_REN_YI_LEE.pdf
http://eprints.utar.edu.my/5393/
_version_ 1769848150249439232
score 13.211869

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