License Plate Detection with Attention-Guided Dual Feature Pyramid Networks in Complex Environments

Abstract

License plate detection plays a significant role in intelligent transportation systems. Convolutional neural networks have shown a remarkable performance and made significant progress for the detection task. Despite these outstanding achievements, license plate detection in complex environments is still a challenging task, due to the noisy background, unpredictable environments and varying shapes and sizes of the license plates. In order to improve the performance of license plate detection in complex environments, we propose a novel approach using an attention-guided dual feature pyramid and a cascaded positioning strategy. At first, the original features of images are extracted by the residual network. In order to make sure that each feature map contains higher- and lower-level semantic information, we utilize a bottom-up and a top-down pathway, respectively. Meanwhile, the proposed attention-guided dual feature pyramid network is used to receive the extracted features for a multilevel feature fusion. Our proposed attention-guided modules contain both spatial and channel attention. Attention-guided modules deduce the attention weights according to channel and spatial dimensions and multiply the calculated result with the input to obtain the refined feature maps. Then, a region proposal network is used to generate the candidate regions for the license plates. Finally, a cascaded positioning network is utilized to obtain the final locations of the license plates. To validate the effectiveness of the proposed method, we conducted a series of experiments on different public datasets. Experiments on AOLP and CCPD validated the effectiveness of our proposed method.