Deep Learning for Retail Product Recognition: Challenges and Techniques. (12th November 2020)

• Record Type: Journal Article
• Authors: Wei, Yuchen; Tran, Son; Xu, Shuxiang; Kang, Byeong; Springer, Matthew
• Other Names: Panella Massimo Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2020(2020)
• Publication Date: 2020-11-12
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2020/8875910
• Languages: English
• ISSNs: 1687-5265

Abstract

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.