How Many Labeled License Plates Are Needed?
Training a good deep learning model often requires a lot of annotated data.As a large amount of labeled data is typically difficult to collect and even more difficult to annotate,data augmentation and data generation are widely used in the process of training deep neural networks.However,there is no clear common understanding on how much labeled data is needed to get satisfactory performance.In this paper,we try to address such a question using vehicle license plate character recognition as an example application.We apply computer graphic scripts and Generative Adversarial Networks to generate and augment a large number of annotated,synthesized license plate images with realistic colors,fonts,and character composition from a small number of real,manually labeled license plate images.Generated and augmented data are mixed and used as training data for the license plate recognition network modified from DenseNet.The experimental results show that the model trained from the generated mixed training data has good generalization ability,and the proposed approach achieves a new state-of-the-art accuracy on Dataset-1 and AOLP,even with a very limited number of original real license plates.In addition,the accuracy improvement caused by data generation becomes more significant when the number of labeled images is reduced.Data augmentation also plays a more significant role when the number of labeled images is increased.
GANs Data augmentation License plate recognition
Changhao Wu Shugong Xu Guocong Song Shunqing Zhang
Shanghai Institute for Advanced Communication and Data Science,Shanghai University,Shanghai 200444,C Playground Global,Palo Alto,USA
国际会议
广州
英文
334-346
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）