Improving Accuracy of Detection and Recognition of Traffic Signs with GANs

The goal of this thesis was to extend a dataset for traffic sign detection. The solution was based on generative neural networks PatchGAN and Wasserstein GAN of combined DenseNet and U-Net architecture. Those models were designed to synthesize real looking traffic signs from images of their norms. Model for object detection SSD, trained on synthetic data only, achieved mean average precision of 59.6 %, which is an improvement of 9.4 % over the model trained on the original data. SSD model trained on synthetic and original data combined achieved mean average precision of 80.1 %