Fast-Speed Image Recognition System on Retail Commodity Image

Meiju Hu, Xiaojing Zhong

Abstract: In recent years, image recognition technology has been widely used in commodity recognition in smart containers and retail shelves. However, as the number of images received by smart terminals is increasing, more and more commodity categories need to be recognized. The image recognition technology adopted by the industry now requires a large number of images, and the modeling time is very long, which cannot meet the requirements for fast and accurate recognition. In this paper, we design and implement a fast-speed image recognition system that can cover all categories of retail commodities to solve the problem. We use DenseNet to strengthen the feature extraction of commodities collected by cameras. At the same time, we utilize a large number of commodity images accumulated in the production system as the primary database to achieve the effect of identifying all categories of commodity. The proposed system can quickly model new commodity images and new packaging of commodities through matching with the database. The experimental results show that the system can overcome the limitation of the number of images and shorten the modeling time. Furthermore, the accuracy rate is not affected in a complex environment.