Detecting Transparency of Glasses with Capsule Networks Based on Deep Learning

Abstract

Object detection applications started to take their places in our lives with the improvements in technology and artificial intelligence. In daily life, some applications such as license plate detection, optical character recognition become indispensable. In parallel with ongoing technological developments today, the technologies which soon will be a part of our daily life such as detection of suspicious situations through security cameras and autonomous cars, are improving rapidly. The studies in the object detection area are have generally focused on opaque objects. The number of studies on transparent objects is very limited. In this study, a system which is trained by the dataset that contains transparent and non-transparent glasses, is used to detect transparency of these glasses. Recently introduced capsule networks are used to develop a proposed system. To compare the obtained results, LeNet, AlexNet, and ResNet are trained and tested with the same dataset. When the result of the study is evaluated, it was seen that CapsNet got better results on classification accuracy than other deep learning methods which are used in this study. The obtained test accuracy, precision, recall, and F-score values for CapsNet are 91.58%, 91.61%, 91.58%, and 91.56%, respectively. The best obtained test accuracies for LeNet, AlexNet, and ResNet are 84.58%, 88.58%, and 87.58%, respectively. Thus, AlexNet got the second-best result on classification accuracy. While the ResNet took third place, LeNet stayed behind all of them.