Simultaneous End-to-End Vehicle and License Plate Detection With Multi-Branch Attention Neural Network

Vehicle and license plate detection plays an important role in intelligent transportation systems and is still a challenging task in real applications, such as on-road scenarios. Recently, Convolutional Neural Network (CNN)-based detectors achieve the state-of-the-art performance. However, it is difficult to efficiently detect the vehicle and license plate simultaneously in most cases. With a single network, the vehicle can affect the detection of the license plate due to the inclusion relation. In this paper, we propose an end-to-end deep neural network for detecting the vehicle and the license plate simultaneously in a given image, where two separate branches with different convolutional layers are designed for vehicle detection and license plate detection, respectively. In consideration of the license plate's small size and fairly obvious features as well as the vehicle's various size and rather complex features, the license plates are detected with low-level features and the vehicles are localized with multi-level features in corresponding convolutional layers. Moreover, a task-specific anchor design strategy is employed to obtain better predictions. Besides, the attention mechanisms and feature-fusion strategies are utilized to improve the detection performance of small-scale objects. A variety of experiments on real datasets and public datasets verify that our proposed method has fairly high accuracy and efficiency.