BigGAN evaluation for the generation of vehicle interior images

Abstract

The number of shared autonomous vehicles (SAV) tends to increase in the coming years, highlighting the need to create monitoring systems that safeguard the integrity of the SAV and the safety of passengers. For the creation of monitoring systems, it is necessary to develop algorithms capable of detecting and classifying a multitude of objects (i.e. dangerous, forgotten, damaged), in different types of vehicles. Currently, deep learning (DL) algorithms present themselves as the best option to solve this problem, but require a large amount of data for training. This article focuses on the use of Generative Adversarial Networks (GAN) for the automatic generation of artificial images of vehicle interiors. Specifically, we propose to employ the BigGAN arquitecture, with the combined implementation of two recent techniques that aim to improve training stability and GAN generalization, namely consistency regularization and differential augmentation. With an expanded version of MoLa-VI dataset (made publicly available), satisfactory results were obtained with the proposed. Moreover, CR+BigGAN combination presented the best results, achieving a Frechet Inception Distance of 28.23 and an Inception Score of 17.19.