Abstract
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.
Abstract (translated)
车牌检测与识别（LPDR）对于实现智能交通、保障城市安全具有越来越重要的意义。然而，在实际环境中，LPDR面临着巨大的挑战。车牌的尺寸、字体和颜色可能非常不同，而且由于拍摄角度倾斜、照明不均匀、遮挡和模糊，车牌图像的质量通常很差。在监视等应用中，通常需要快速处理。为了实现车牌识别的实时性和准确性，本文提出了一套新的车牌识别技术：1）轮廓重建方法，结合边缘检测，快速检测候选车牌；2）简单的零一变换方案，有效地去除车牌周围的假上下边界，使车牌识别更加准确。板上字符的定位；3）一套增强训练数据的技术，将筛选特征纳入CNN网络，利用转移学习获得初始参数，以便更有效地训练；4）一个以低成本确定正确板的两阶段验证程序，板上数据的统计过滤。检测阶段，快速去除不需要的候选者，并准确的CR结果后，CR工艺进行进一步的板验证，无需额外的处理。我们基于我们的算法实现了一个完整的LPDR系统。实验结果表明，该系统能够实时准确地识别车牌。此外，它在不同的照明和噪音水平下，以及在车辆移动的情况下，工作稳定。与同行方案相比，我们的系统不仅是最精确的方案，而且速度最快，可以很容易地应用到其他方案中。
URL
https://arxiv.org/abs/1906.04376
