The technology that can recognize the license plates of vehicles in real time and manage them automatically is a key element of building an intelligent transportation system. License plate recognition is the most important technique in vehicle image processing used to identify a vehicle. Object recognition using a camera is greatly influenced by environmental factors in which the camera is installed. When the vehicle image is acquired, the image is distorted due to the tilting of the license plate, reflection of light, lighting effects, rainy weather, and nighttime, so that it is difficult to accurately recognize the license plate. In addition, when the geometric distortion of the license plate image or the degradation of the image quality is intensified, it may be more difficult to automatically recognize the license plate image. Therefore, in this paper, we propose a deep learning–based vehicles’ license plate recognition method to detect license plate and recognize characters accurately in complex and diverse environments. As a deep learning model, the YOLO model can be used to detect robust license plates in a variety of environments and to recognize characters quickly and accurately. It can also be seen that the license plate accurately recognizes the license plate with geometric distortion.

能够实时识别车辆牌照并自动进行管理的技术是构建智能交通系统的关键要素。车牌识别是用于识别车辆的车辆图像处理中最重要的技术。使用相机进行物体识别受安装相机的环境因素的影响很大。当获取车辆图像时，图像由于车牌的倾斜，光的反射，照明效果，阴雨天气和夜间而失真，从而难以准确地识别车牌。另外，当车牌图像的几何失真或图像质量的劣化加剧时，可能更加难以自动识别车牌图像。所以，在本文中，我们提出了一种基于深度学习的车牌识别方法，以在复杂多样的环境中检测车牌并准确识别字符。作为深度学习模型，YOLO模型可用于在各种环境中检测坚固的车牌并快速准确地识别字符。还可以看出，车牌准确地识别出具有几何变形的车牌。