License Plate Detection with Shallow and Deep CNNs in Complex Environments

Abstract

License plate detection is a challenging problem due to the large visual variations in complex environments, such as motion blur, occlusion, and lighting changes. An advanced discriminative model is needed to accurately segment license plates from the backgrounds. However, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose to detect license plate based on two CNNs, a shallow CNN and a deep CNN. The shallow CNN is used to quickly remove most of the background regions to reduce the computation cost, and the deep CNN is used to detect license plate in the remaining regions. These two CNNs are trained end to end and are complementary to each other to guarantee the detection precision with low computation cost. Experimental results show that the proposed method is promising for license plate detection.