License Plate Image Generation Method Based on Cycleflow-GAN
With the rapid development of deep learning-based methods in intelligent transportation systems,li-cense plate character recognition also plays a crucial role.However,insufficient license plate image samples can lead to poor performance of the recognition model.In this work,an image generation method based on Cycleflow-GAN is proposed,where the generative network is constructed by a series of invertible transformation functions with simple structure and its interpretability is strong.The complexity of the loss function is also optimized and the least squares loss is used to make the training of the model more stable.The results show that the quality of the generated images is improved,and the generated diverse license plate maps can meet the training of large samples for license plate character recognition.
万方数据
维普
