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.

中文翻译：

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零售产品识别的深度学习：挑战和技术


花时间识别想要的产品以及在零售店等待收银台是我们日常生活中经常遇到的场景。产品自动识别的实现对于经济和社会进步都具有重要意义，因为它比人工操作更可靠、更省时。通过图像进行产品识别是计算机视觉领域的一项具有挑战性的任务。由于其巨大的应用前景，例如自动结帐、库存跟踪、货架图合规性和视障辅助等，它受到越来越多的关注。近年来，深度学习蓬勃发展，在图像分类和目标检测方面取得了巨大成就。本文旨在对基于深度学习的零售产品识别的最新研究进行全面的文献综述。更具体地说，本文回顾了零售产品识别深度学习的主要挑战，并讨论了有助于该主题研究的潜在技术。接下来，我们提供可用于深度学习的公共数据集的详细信息。最后，我们总结了当前的进展，并为相关领域的研究提出了新的视角。