License plate detection and recognition (LPDR) is of growing importance for enabling intelligent transportation and ensuring the security and safety of the cities. However, LPDR faces a big challenge in a practical environment. The license plates can have extremely diverse sizes, fonts and colors, and the plate images are usually of poor quality caused by skewed capturing angles, uneven lighting, occlusion, and blurring. In applications such as surveillance, it often requires fast processing. To enable real-time and accurate license plate recognition, in this work, we propose a set of techniques: 1) a contour reconstruction method along with edge-detection to quickly detect the candidate plates; 2) a simple zero-one-alternation scheme to effectively remove the fake top and bottom borders around plates to facilitate more accurate segmentation of characters on plates; 3) a set of techniques to augment the training data, incorporate SIFT features into the CNN network, and exploit transfer learning to obtain the initial parameters for more effective training; and 4) a two-phase verification procedure to determine the correct plate at low cost, a statistical filtering in the plate detection stage to quickly remove unwanted candidates, and the accurate CR results after the CR process to perform further plate verification without additional processing. We implement a complete LPDR system based on our algorithms. The experimental results demonstrate that our system can accurately recognize license plate in real-time. Additionally, it works robustly under various levels of illumination and noise, and in the presence of car movement. Compared to peer schemes, our system is not only among the most accurate ones but is also the fastest, and can be easily applied to other scenarios.
翻译:汽车牌照的探测和识别(LPDR)对于智能交通和确保城市的安保和安全越来越重要,然而,LPDR在实际环境中面临巨大的挑战。牌照的大小、字体和颜色可能非常不同,牌照的图像通常质量差,原因是拍摄角度偏斜、照明不均、排斥和模糊。在监视等应用中,它往往需要快速处理。在这项工作中,为了能够实时准确识别牌照,我们建议一套技术:(1) 与边缘探测一起进行轮廓重建,以快速检测候选人的牌照;(2) 简单零一对齐计划,以有效清除牌照周围的假顶部和底边界,以便于更准确地分割牌照上的人物;(3) 一套技术,用以增加培训数据,将SIFT特征纳入CNN网络,利用转移学习获得初步参数,以便进行更有效的培训;(4) 一种两阶段核查程序,以便以低成本确定正确的板牌牌,在板盘检测阶段进一步应用统计过滤,以迅速清除不想要的候选人;(2) 一个简单的零一对牌照对象实行零一板的升级计划,而CRRR的准确的核查结果在我们的实验室里,然后才能进行。