Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence
Unstaffed retail shops have emerged recently and been noticeably changing our shopping styles. In terms of these shops, the design of vending machine is critical to user shopping experience. The conventional design typically uses weighing sensors incapable of sensing what the customer is taking. In...
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doaj-c26287597fc64320a8fd05fc793c31ef2021-03-30T01:55:24ZengIEEEIEEE Access2169-35362020-01-01814772814773710.1109/ACCESS.2020.30140479154684Design of Smart Unstaffed Retail Shop Based on IoT and Artificial IntelligenceJianqiang Xu0https://orcid.org/0000-0002-6554-2041Zhujiao Hu1Zhuo Zou2https://orcid.org/0000-0002-8546-1329Junzhong Zou3https://orcid.org/0000-0001-8119-8503Xiaoming Hu4https://orcid.org/0000-0003-0177-1993Lizheng Liu5https://orcid.org/0000-0002-6554-2041Lirong Zheng6East China University of Science and Technology, Shanghai, ChinaSchool of Information Science and Technology, Fudan University, Shanghai, ChinaSchool of Information Science and Technology, Fudan University, Shanghai, ChinaEast China University of Science and Technology, Shanghai, ChinaOptimization and System Theory Department of Mathematics, Royal Institute of Technology (KTH), Stockholm, SwedenSchool of Information Science and Technology, Fudan University, Shanghai, ChinaSchool of Information Science and Technology, Fudan University, Shanghai, ChinaUnstaffed retail shops have emerged recently and been noticeably changing our shopping styles. In terms of these shops, the design of vending machine is critical to user shopping experience. The conventional design typically uses weighing sensors incapable of sensing what the customer is taking. In the present study, a smart unstaffed retail shop scheme is proposed based on artificial intelligence and the internet of things, as an attempt to enhance the user shopping experience remarkably. To analyze multiple target features of commodities, the SSD ($300\times 300$ ) algorithm is employed; the recognition accuracy is further enhanced by adding sub-prediction structure. Using the data set of 18, 000 images in different practical scenarios containing 20 different type of stock keeping units, the comparison experimental results reveal that the proposed SSD ($300\times 300$ ) model outperforms than the original SSD ($300\times 300$ ) in goods detection, the mean average precision of the developed method reaches 96.1% on the test dataset, revealing that the system can make up for the deficiency of conventional unmanned container. The practical test shows that the system can meet the requirements of new retail, which greatly increases the customer flow and transaction volume.https://ieeexplore.ieee.org/document/9154684/Unstaffed retailInternet of Thingsvending machineartificial intelligenceSSD |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianqiang Xu Zhujiao Hu Zhuo Zou Junzhong Zou Xiaoming Hu Lizheng Liu Lirong Zheng |
spellingShingle |
Jianqiang Xu Zhujiao Hu Zhuo Zou Junzhong Zou Xiaoming Hu Lizheng Liu Lirong Zheng Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence IEEE Access Unstaffed retail Internet of Things vending machine artificial intelligence SSD |
author_facet |
Jianqiang Xu Zhujiao Hu Zhuo Zou Junzhong Zou Xiaoming Hu Lizheng Liu Lirong Zheng |
author_sort |
Jianqiang Xu |
title |
Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence |
title_short |
Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence |
title_full |
Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence |
title_fullStr |
Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence |
title_full_unstemmed |
Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence |
title_sort |
design of smart unstaffed retail shop based on iot and artificial intelligence |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Unstaffed retail shops have emerged recently and been noticeably changing our shopping styles. In terms of these shops, the design of vending machine is critical to user shopping experience. The conventional design typically uses weighing sensors incapable of sensing what the customer is taking. In the present study, a smart unstaffed retail shop scheme is proposed based on artificial intelligence and the internet of things, as an attempt to enhance the user shopping experience remarkably. To analyze multiple target features of commodities, the SSD ($300\times 300$ ) algorithm is employed; the recognition accuracy is further enhanced by adding sub-prediction structure. Using the data set of 18, 000 images in different practical scenarios containing 20 different type of stock keeping units, the comparison experimental results reveal that the proposed SSD ($300\times 300$ ) model outperforms than the original SSD ($300\times 300$ ) in goods detection, the mean average precision of the developed method reaches 96.1% on the test dataset, revealing that the system can make up for the deficiency of conventional unmanned container. The practical test shows that the system can meet the requirements of new retail, which greatly increases the customer flow and transaction volume. |
topic |
Unstaffed retail Internet of Things vending machine artificial intelligence SSD |
url |
https://ieeexplore.ieee.org/document/9154684/ |
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