Traffic Sign Image Synthesis with Generative Adversarial Networks
Hengliang Luo, Qingqun Kong, Fuchao Wu · 2018
Deep convolutional neural networks (CNN) has achieved state-of-the-art result on traffic sign classification, which plays a key role in intelligent transportation system. However, it usually requires a large number of labeled training data, which is not always available, to guarantee a good performance. In this paper, we propose to synthesize traffic sign images by generative adversarial networks (GANs). It takes a standard traffic sign template and a background image as input to the generative network in GANs, where the template defines which class of traffic sign to include and the background image controls the visual appearance of the synthetic images. Experiments show that our method could generate more realistic traffic sign images than the conventional image synthesis method. Meanwhile, by adding the synthesis images to train a typical CNN for traffic sign classification, we obtained a better accuracy.