Liu Chengxu, Da Zongyang, Liang Yuanzhi, Xue Yao, Zhao Guoshuai, Qian Xueming
IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):1584-1597. doi: 10.1109/TNNLS.2022.3184075. Epub 2024 Feb 5.
Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categories product recognition based on intelligent UVMs. It can be divided into two parts: 1) first, we explore the similarities and differences between products through manifold learning, and then we build a hierarchical multigranularity label to constrain the learning of representation; and 2) second, we propose a hierarchical label object detection network, which mainly includes coarse-to-fine refine module (C2FRM) and multiple granularity hierarchical loss (MGHL), which are used to assist in capturing multigranularity features. The highlights of our method are mine potential similarity between large-scale category products and optimization through hierarchical multigranularity labels. Besides, we collected a large-scale product recognition dataset GOODS-85 based on the actual UVMs scenario. Experimental results and analysis demonstrate the effectiveness of the proposed product recognition methods.
近年来，“无人零售”这一新兴概念越来越受到关注，基于智能无人售货机（UVM）场景的无人零售具有巨大的市场需求。然而，现有的智能UVM产品识别方法无法适应大规模类别，且准确性不足。在本文中，我们提出了一种基于智能UVM的大规模类别产品识别方法。它可分为两部分：1）首先，我们通过流形学习探索产品之间的异同，然后构建分层多粒度标签来约束表征学习；2）其次，我们提出了一种分层标签目标检测网络，主要包括从粗到精细化模块（C2FRM）和多粒度分层损失（MGHL），用于辅助捕捉多粒度特征。我们方法的亮点在于挖掘大规模类别产品之间的潜在相似性，并通过分层多粒度标签进行优化。此外，我们基于实际UVM场景收集了一个大规模产品识别数据集GOODS-85。实验结果与分析证明了所提产品识别方法的有效性。
