摘要： Automatic License Plate Recognition (ALPR) is one of the most important methods of intelligent traffic surveillance applications. Some existing ALPR systems are developed for near-frontal plate images in a single lane. However, most surveillance cameras have a challenging environment: small size object, poor resolution and blurred image. We propose a new method that can be applied in the ALPR challenged environments by using super-resolution (SR) module based on Generative Adversarial Networks (GAN). We also used the state-of-the-art and real-time object detection method, You Only Look Once (YOLO), for license plate detection and character recognition. We collected a challenging dataset at low resolution and small object less than 60*60 size and evaluate our approach on it. The achieved mean accuracy of recognition of license plate is above 2% better than other methods in our dataset. Our implementation demonstrate the superiority over the state-of-the-art.