Improving the efficiency of neural networks with virtual training data

Abstract

At Széchenyi István University, an autonomous racing car for the Shell Eco-marathon is being developed. One of the main tasks is to create a neural network which segments the road surface, protective barriers and other components of the racing track. The difficulty with this task is that no suitable dataset for special objects, e.g. protective barriers, exists. Only a dataset limited in terms of its size is available, therefore, computer-generated virtual images from a virtual city environment are used to expand this dataset. In this work, the effect of computer-generated virtual images on the efficiency of different neural network architectures is examined. In the training process, real images and computer-generated virtual images are mixed in several ways. Subsequently, three different neural network architectures for road surfaces and the detection of protective barriers are trained. Past experiences determine how to mix datasets and how they can improve efficiency.