End-to-End Light License Plate Detection and Recognition Method Based on Deep Learning

Abstract

In the field of intelligent robot and automatic drive, the task of license plate detection and recognition (LPDR) are undertaken by mobile edge computing (MEC) chips instead of large graphics processing unit (GPU) servers. For this kind of small computing capacity MEC chip, a light LPDR network with good performance in accuracy and speed is urgently needed. Contemporary deep learning (DL) LP recognition methods use two-step (i.e., detection network and recognition network) or three-step (i.e., detection network, character segmentation method, and recognition network) strategies, which will result in loading two networks on the MEC chip and inserting many complex steps. To overcome this problem, this study presents an end-to-end light LPDR network. Firstly, this network adopts the light VGG16 structure to reduce the number of feature maps and adds channel attention at the third, fifth, and eighth layers. It can reduce the number of model parameters without losing the accuracy of prediction. Secondly, the prediction of the LP rotated angle is added, which can improve the matching between the bounding box and the LP. Thirdly, the LP part of the feature map is cropped by the relative position of detection module, and the region-of-interest (ROI) pooling and fusion are performed. Seven classifiers are then used to identify the LP characters through the third step’s fusion feature. At last, experiments show that the accuracy of the proposed network reaches 91.5 and that the speed reaches 63 fps. In the HiSilicon 3516DV300 and the Rockchip Rv1126 Mobile edge computing chips, the speed of the network has been tested for 15 fps.