A Recognition Algorithm Suitable for Complex Scenes and Various Types of License Plates

Abstract

With the acceleration of intelligent transportation in the new era, license plate recognition technology has become more and more extensive. Although the existing license plate recognition technology can show good performance for specific scenes and traditional license plate recognition, it has poor recognition robustness and applicability in complex external environments such as arbitrary shooting angles, different lighting conditions, and different picture quality. To solve the above problems, this paper proposes an improved recognition algorithm suitable for complex scenes and various types of license plates. First, the SSD model is used for multi-target vehicle detection; the convolutional neural network (CNN) is further used for license plate location and correction; finally, the YOLOv2 model with modified output and reduced pooling layer is designed to realize the license plate character recognition. The algorithm in this paper is carried out on the surveillance image data of a road in a block of Beijing, which contains 3016 images with a total of 5315 vehicles to be detected. The experimental results show that the accuracy rate of the license plate recognition of the proposed method reaches 95.66%, and it has considerable detection performance for traditional license plates and new energy license plates. Compared with the classic algorithm HyperLPR, the recognition accuracy of the proposed algorithm in complex scenarios is improved by 13 %.