Deep learning-based beverage recognition for unmanned vending machines: An empirical study

In recent years, deep learning techniques have been commonly used in the fields of image processing and computer vision. With the popularity of deep learning models, researchers have developed many effective object detection methods. Unmanned retail applications start to utilizing object detection algorithms for changing traditional retail modes. Until now, there is no public datasets for object detection in unmanned retail application environments. Moreover, state-of-the-art deep learning-based object detection models have not yet been examined in this application scenario. In this paper, we compiled a large-scale dataset which contains over 30,000 images captured in a refrigerator equipped with different cameras. 10 kinds of beverages were utilized for targeted objects. An empirical study on this dataset is performed by using several recent developed deep learning models. Results demonstrate the effectiveness of using deep learning techniques real-life unmanned retail environments.