TY - GEN
T1 - A Smart Unstaffed Retail Shop Based on Artificial Intelligence and IoT
AU - Liu, Lizheng
AU - Zhou, Bo
AU - Zou, Zhuo
AU - Yeh, Shih Ching
AU - Zheng, Lirong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/29
Y1 - 2018/10/29
N2 - Unstaffed retail shop has been emerging in the past years and significantly affected conventional shopping styles. In this area, unmanned retail container plays an important role, it can greatly influence the user shopping experience, the traditional way based on weighing sensors cannot sense what the customer is taking. This paper proposes a smart unstaffed retail shop scheme based on artificial intelligence (AI) and the internet ofthings (IoT), aiming at exploring the feasibility of implementing the unstaffed retail shopping style. Based on the data set of 11, 000 images in different scenarios that containing 10 different types of stock keeping unit (SKU), an end-to-end classification model trained by the MASK-RCNN method is developed for SKU counting and recognition, and the proposed solution in this study is able to achieve 97.7% counting accuracy and 98.7% recognition accuracy on the test dataset, which indicates that the system can make up for the deficiency of traditional unmanned container.
AB - Unstaffed retail shop has been emerging in the past years and significantly affected conventional shopping styles. In this area, unmanned retail container plays an important role, it can greatly influence the user shopping experience, the traditional way based on weighing sensors cannot sense what the customer is taking. This paper proposes a smart unstaffed retail shop scheme based on artificial intelligence (AI) and the internet ofthings (IoT), aiming at exploring the feasibility of implementing the unstaffed retail shopping style. Based on the data set of 11, 000 images in different scenarios that containing 10 different types of stock keeping unit (SKU), an end-to-end classification model trained by the MASK-RCNN method is developed for SKU counting and recognition, and the proposed solution in this study is able to achieve 97.7% counting accuracy and 98.7% recognition accuracy on the test dataset, which indicates that the system can make up for the deficiency of traditional unmanned container.
KW - Artificial Intelligence
KW - Internet of Things
KW - MASK-RCNN
KW - deep learning
KW - unstaffed retail
UR - http://www.scopus.com/inward/record.url?scp=85057222359&partnerID=8YFLogxK
U2 - 10.1109/CAMAD.2018.8514988
DO - 10.1109/CAMAD.2018.8514988
M3 - Conference contribution
AN - SCOPUS:85057222359
T3 - IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
BT - 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2018
Y2 - 17 September 2018 through 19 September 2018
ER -