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Deep learning for retail product recognition: challenges and techniques


Wei, Y and Tran, S and Xu, S and Kang, B and Springer, M, Deep learning for retail product recognition: challenges and techniques, Computational Intelligence and Neuroscience, 2020 Article ID 8875910. ISSN 1687-5265 (2020) [Refereed Article]


Copyright Statement

Copyright 2020 Yuchen Wei et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.1155/2020/8875910


Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.

Item Details

Item Type:Refereed Article
Keywords:retail product recognition, deep learning, image processing
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Computer vision
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Wei, Y (Dr Yuchen Wei)
UTAS Author:Tran, S (Dr Son Tran)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Kang, B (Professor Byeong Kang)
UTAS Author:Springer, M (Dr Matthew Springer)
ID Code:141730
Year Published:2020
Web of Science® Times Cited:9
Deposited By:Information and Communication Technology
Deposited On:2020-11-13
Last Modified:2020-12-10
Downloads:19 View Download Statistics

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