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Deep learning for retail product recognition: challenges and techniques

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Wei, Y, Tran, S ORCID: 0000-0002-5912-293X, Xu, S ORCID: 0000-0003-0597-7040, Kang, B ORCID: 0000-0003-3476-8838 and Springer, M ORCID: 0000-0003-3017-2893 2020 , 'Deep learning for retail product recognition: challenges and techniques' , Computational Intelligence and Neuroscience, vol. 2020 , pp. 1-23 , doi: 10.1155/2020/8875910.

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Abstract

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 Type: Article
Authors/Creators:Wei, Y and Tran, S and Xu, S and Kang, B and Springer, M
Keywords: retail product recognition, deep learning, image processing
Journal or Publication Title: Computational Intelligence and Neuroscience
Publisher: Hindawi Limited
ISSN: 1687-5265
DOI / ID Number: 10.1155/2020/8875910
Copyright Information:

Copyright 2020 Yuchen Wei et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

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