Brand Recognition with Partial Visible Image in the Bottle Random Picking Task based on Inception V3 In the brandwise randomordered drinking PET bottles picking task, the overlapping and viewing angle problem makes a low accuracy of the brand recognition. In this paper, we set the problem to increase the brand recognition accuracy and try to find out how the overlapping rate infects on the recognition accuracy. By using a stepping motor and transparent fixture, the training images were taken automatically from the bottles under 360 degrees to simulate a picture taken from viewing angle. After that, the images are augmented with random cropping and rotating to simulate the overlapping and rotation in a real application. By using the automatically constructed dataset, the Inception V3, which was transferred learning from ImageNet, is trained for brand recognition. By generating a random mask with a specific overlapping rate on the original image, the Inception V3 can give 80% accuracy when 45% of the object in the image is visible or 86% accuracy when the overlapping rate is lower than 30%.

Deep Learningbased 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, stateoftheart deep learningbased object detection models have not yet been examined in this application scenario. In this paper, we compiled a largescale 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 reallife unmanned retail environments.