Image Generation Framework for Unbalanced License Plate Data Set Deep learning based methods have achieved promising performance in the image fields, but they are significantly sensitive to the distribution of data. To obtain balanced data, the acquisition of annotation data is very timeconsuming and laborious. Generative Adversarial Networks have made a lot of progress in data generation but may degrade greatly in the unbalanced data set. In this paper, we propose an image generation framework to generate photorealistic, various, and balanced images of text. Specifically, we add the module for training unpaired images (Umodule) and target selector to our framework, and the target selector uses text string to select images in the extended real images, which contain real images and the generated images by the Umodule, in addition, global generator and local enhancer network are applied to improve the quality of the generated images. We demonstrate our method on the Chinese license plates, and the unbalance of the license plate data set is shown in the provincial category and the special license plates. The Inception Score and the FID Score are used as metrics to validate our method. The experimental results show that the Inception Score of the generated images is close to that of the real images, and our method achieves the lower FID Score than the stateoftheart. In the SYSUITS dataset, the accuracy of license plate recognition has been largely improved, especially for the provinces with no or few real samples.

Unconstrained Road Marking Recognition with Generative Adversarial Networks Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often overdependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates highquality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a classconditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.