A Hybrid Deep Learning Algorithm for the License Plate Detection and Recognition in Vehicle-to-Vehicle Communications
Xiuqin Pan, Sumin Li, Ruixiang Li, Na Sun
Abstract
With the rapid development of Internet of Things (IoT) in the field of transportation, the vehicle-to-vehicle (V2V) communication not only becomes available on a large scale, but also will be an indispensable part of the future transportation. License plates are the identification of vehicles, so the license plate detection and recognition in the V2V communication scenario is very important. However, the existing license plate detection and recognition methods are suffering from a low accuracy rate issue. To solve this issue, we propose a hybrid deep learning algorithm as the license plate detection and recognition model by fusing YOLOV3 and CRNN. The proposed model enables the network itself to better utilize the different fine-grained features in the high and low layers to carry out multi-scale detection and recognition. In this model, we utilize the fast and accurate performance of YOLOV3, and the excellent detection ability of CRNN. As a result, this proposed model reaps the benefit of both. Finally, we test this proposed model in difficult scenarios and low-quality license plate images caused by weather, and results show this proposed license plate detection and recognition model can achieve a higher mean average precision, better comprehensive performance, and excellent robustness.