The effect of virtual reality images on artificial intelligence classification of real environment images

Since virtual reality (VR) is a new and current field of study, it is intensively studied by researchers. Health, education, engineering, culture and tourism, architecture, military fields and many other fields of study have taken steps to support their studies with VR technology and the related subject has become the focus of many researchers. In this thesis, VR technology was used to improve the classification performance of real media images. The proposed approach consists of the classification of real environment images with transfer learning. The transfer learning mentioned in the thesis study can be defined as training an untrained deep architecture with VR images and then retraining the network with real images (fine-tuning). For this purpose, VR scenes are designed in the UNITY environment. A data set consisting of 15 environments, called V-Env15, was prepared from the designed VR scenes. The proposed approach was tested with the Scene-15 data set, which is frequently used in environment classification studies. In the thesis, serial and parallel network and GoogLeNet and Inception-ResNet-V2 deep learning architectures were used. In the experimental studies, 0.56% higher accuracy performance increase was achieved in the serial architecture and 4.68% higher accuracy performance increase in parallel architecture. A 4.79% increase was achieved between GoogLeNet and the Serial network, and a 0.44% decrease was achieved between the Parallel network. A 4.47% increase was achieved between Inception-ResNet-V2 and the Serial network, and a 4.57% decrease was achieved between the Parallel network.