Vision Inspection of Bottle Caps in Drink Factories Using Convolutional Neural Networks Nowadays, the growth and development of artificial intelligence is more evident than any other time. Consequently, the development of machine vision systems in the industry has been studied extensively. The provision of a highlevel quality control system based on vision inspection in production lines is an important issue. It not only can increase the efficiency, but also derives the necessary tools to gather information about technical errors and faults in the target system. In this paper, we focus on the vision inspection of bottle caps in drink factories using convolutional neural networks. There are different cap encapsulation statuses which are classified as Normal Cap, Unfixed Cap, and No Cap. According to these classes, we can make a decision between either acceptance or rejection of a given bottle. Achieved results show that our optimized finetuning method based on the VGG19 as an EndtoEnd deep learning approach can lead to more than 99% accuracy.

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.