Citation

Lee, Ren Yi and Chai, Tong Yuen and Chua, Sing Yee and Lai, Yen Lung and Sim, Yee Wai and Haw, Su Cheng (2022) Cashierless Checkout Vision System for Smart Retail using Deep Learning. Journal of System and Management Sciences, 12 (4). pp. 232-250. ISSN 1816-6075, 1818-0523

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Abstract

As Corona Virus Disease (COVID-19) pandemic strikes the world, retail industry has been severely impacted by staff shortage and high risk of virus outbreak. However, most of existing smart retail solutions is associated with high deployment and maintenance cost that are infeasible for small retail stores. As an effort to mitigate the issue, a computer vision-powered smart cashierless checkout system is proposed based on You Only Look Once (YOLO) v5 and MobileNet V3 for product recognition along with 3-stage image synthesis framework that includes crop and paste algorithm, GAN-based shadow synthesis and light variation algorithm. By using 3000 images generated from the framework, proposed model was trained and optimized with TensorRT. Experimental result shows that the lightweight model can be deployed on affordable edge devices like Jetson Nano while achieving high Mean Average Precision (mAP) of 98.2%, Checkout Accuracy (cAcc) of 89.17% with only 0.142s of inference time.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, object detection, deep learning, retail stores, smart retail
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business > HF5428-5429.6 Retail trade
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	11 Oct 2022 05:31
Last Modified:	11 Oct 2022 05:31
URII:	http://shdl.mmu.edu.my/id/eprint/10526

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