Moussaoui Hanae, Akkad Nabil El, Benslimane Mohamed, El-Shafai Walid, Baihan Abdullah, Hewage Chaminda, Rathore Rajkumar Singh
Engineering Systems and Applications Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Laboratory of Industrial Techniques (LTI), EST of Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Sci Rep. 2024 Jun 22;14(1):14389. doi: 10.1038/s41598-024-65272-1.
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.
车辆识别系统是当代生活诸多方面的关键组成部分，比如安全、贸易、交通和执法等。它们通过加强车辆管理、安保和透明度来提升社区和个人的福祉。这些任务需要运用计算机视觉和机器学习技术从图像或视频帧中定位并提取车牌，然后识别车牌上的字母或数字。本文提出了一种基于深度学习YOLO v8方法、图像处理技术和用于文本识别的OCR技术的新型车牌检测与识别方法。为此，第一步是创建数据集，从互联网上收集了270张图像。之后，使用CVAT（计算机视觉标注工具）对数据集进行标注，它是一个开源软件平台，旨在使计算机视觉任务中对图像和视频的标注与标记更加简便。随后，采用新发布的Yolo版本Yolo v8来检测输入图像中的车牌区域。接着，在提取车牌后，使用k均值聚类算法、阈值处理技术和开运算形态学操作来增强图像，并在使用OCR之前使车牌上的字符更清晰。此过程的下一步是使用OCR技术提取字符。最终，生成一个仅包含反映车辆所属国家字符的文本文件。为了提高所提方法的效率，采用了几个指标，即精度、召回率、F1分数和CLA。此外，还将所提方法与文献中的现有技术进行了比较。所提方法在检测和识别方面均取得了令人信服的结果，检测准确率达到99%，字符识别准确率达到98%。
