ABSTRACT
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.
