A Smart Unstaffed Retail Shop Based on Artificial Intelligence and IoT

Unstaffed retail shop has been emerging in the past years and significantly affected conventional shopping styles. In this area, unmanned retail container plays an important role, it can greatly influence the user shopping experience, the traditional way based on weighing sensors cannot sense what the customer is taking. This paper proposes a smart unstaffed retail shop scheme based on artificial intelligence (AI) and the internet ofthings (IoT), aiming at exploring the feasibility of implementing the unstaffed retail shopping style. Based on the data set of 11, 000 images in different scenarios that containing 10 different types of stock keeping unit (SKU), an end-to-end classification model trained by the MASK-RCNN method is developed for SKU counting and recognition, and the proposed solution in this study is able to achieve 97.7% counting accuracy and 98.7% recognition accuracy on the test dataset, which indicates that the system can make up for the deficiency of traditional unmanned container.