Deep learning technology is increasingly applied in vehicle license plate recognition. However, when training the model, there is a lack of data under different environments. To address this problem, several different Generative adversarial networks were applied to generate more vehicle license plate data in different environments, including low light environment, fuzzy environment, environment of bad shooting angles and environment of license plate fouling etc. Results showed that generated license plate data by CycleGAN in different environments had a good performance, which closed to real data in style migration. Wasserstein GAN (WGAN) not only the greater stability and high generalization can be achieved, but also the realistic images were produced. Deep Convolution Generative Adversarial Network (DCGAN) also generated real images but it was difficult to train. Generative adversarial networks (GAN) often had the problem of model collapse, so the ideal images cannot be generated. The better confrontation network selected in a more complex environment to extend the data set preprocessing work has great significance to improve the recognition rate of vehicle license plate recognition technology through this research.
Fast acquisition and processing of effective data sources are a heated topic in remote sensing image processing research. Unmanned aerial vehicle (UAV) remote sensing system has the advantages of maneuverability, rapidity and economical, it has become a hot topic in the world. The study analyzes the characteristics of remote sensing image and the characteristics of UAV remote sensing system, and refers a variety of images fast processing algorithms to explore the rapid remote sensing images stitching and rapid information extraction methods. Based on the analysis of the relevant research at home and abroad, this paper draws lessons from some image processing ideas of modern photogrammetry, and proposes a fast image stitching method of UAV remote sensing images based on SURF (Speed Up Robust Features) feature description. This method is applied to UAV remote sensing fast image stitching to achieve high-quality UAV remote sensing images for fast and automatic splicing. The stitching speed of this method is much faster than that of SIFT (Scale-invariant feature transform) algorithm. And the splicing effect of this method is satisfactory.