Building Unmanned Store Identification Systems Using YOLOv4 and Siamese Network
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Applied Sciences, 2022, 12, (8), pp. 3826
- Issue Date:
- 2022-04-01
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Labor is the most expensive in retail stores In order to increase the profit of retail stores unmanned stores could be a solution for reducing labor cost Deep learning is a good way for recog nition classification and so on in particular it has high accuracy and can be implemented in real time Based on deep learning in this paper we use multiple deep learning models to solve the problems often encountered in unmanned stores Instead of using multiple different sensors only five cameras are used as sensors to build a high accuracy low cost unmanned store for the full use of space we then propose a method for calculating stacked goods so that the space can be effectively used For checkout without a checking counter we use a Siamese network combined with the deep learning model to directly identify products instantly purchased As for protecting the store from theft a new architecture was proposed which can detect possible theft from any angle of the store and prevent unnecessary financial losses in unmanned stores As all the customers buying records are identified and recorded in the server it can be used to identify the popularity of the product In particular it can reduce the stock of unpopular products and reduce inventory 2022 by the authors Licensee MDPI Basel Switzerland
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