On Automatic Generation of Training Images for Machine Learning in Automotive Applications

Machine learning is expected to play an important role in implementing automotive systems such as the Advanced Driver Assistance Systems (ADAS). To make machine learning methods work well, providing a sufficient number of training data is very important. However, collecting the training data may be difficult or very timing-consuming. In this paper we investigate automatic generation of training data for automotive applications. The Generative Adversarial Network (GAN) techniques are employed to generate fake yet still high-quality data for machine learning. Although using GAN to generate training images has been proposed in the literature, the previous work does not consider automotive applications. In this work a case study on vehicle detection is provided to demonstrate powerfulness of GAN and the effectiveness of the generated training images by GAN. The generated fake bus images are employed as training data and a SVM (Support Vector Machine) method is implemented to detect buses. The results show that the SVM trained by the fake images achieves almost the same detection accuracy as that by real images. The result also shows that GAN can generate the training images very fast. The extension of GAN to generate road images with various weather conditions such as fogs or nights is also discussed.