车辆识别系统是实现当代生活的许多方面的重要组成部分,比如安全,贸易,transit,和执法。他们通过增加车辆管理来改善社区和个人福祉,安全,和透明度。这些任务需要使用计算机视觉和机器学习技术从图像或视频帧中定位和提取车牌,然后识别盘子上的字母或数字。本文提出了一种基于深度学习YOLOv8方法的车牌检测与识别方法,图像处理技术,以及用于文本识别的OCR技术。为此,第一步是创建数据集,当从互联网上收集270张图像时。之后,CVAT(计算机视觉注释工具)用于注释数据集,这是一个开源软件平台,旨在使计算机视觉任务更容易注释和标记图像和视频。随后,新发布的Yolo版本,Yolov8已用于检测输入图像中的车牌区域。随后,提取板块后的k-均值聚类算法,阈值技术,在使用OCR之前,使用打开形态学操作来增强图像并使车牌中的字符更清晰。此过程的下一步是使用OCR技术来提取字符。最终,生成仅包含反映车辆国家/地区的字符的文本文件。为了提高所提出方法的效率,采用了几个指标,即精度,召回,F1-Score,和CLA。此外,已将所提出的方法与文献中的现有技术进行了比较。通过获得99%的检测精度和98%的字符识别精度,建议的方法在检测和识别中都获得了令人信服的结果。
Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the
thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle\'s country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.