Deep learning based transparent object detection

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. However, there are transparent objects as well as opaque objects around us, and the detection of transparent objects is also important. Transparent object detection will contribute to the perception of the environment of the robots that will soon take place in our lives and the sorting process of the objects in the recycling facilities. 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 deep learning approach 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 that are used in this study. According to the classification accuracy, other methods were lined up as AlexNet, ResNet, and LeNet. As a result of the study, it has been seen that capsule networks can be used in transparent object detection problems and reach higher accuracy rates than existing methods.