In vision-driven Intelligent Transportation Systems (ITS) where cameras play a vital role, accurate detection and re-identification of vehicles are fundamental demands. Hence, recent approaches have employed a wide range of algorithms to provide the best possible accuracy. These methods commonly generate a vehicle detection model based on its visual appearance features such as license plate, headlights, or some other distinguishable specifications. Among different object detection approaches, Deep Neural Networks (DNNs) have the advantage of magnificent detection accuracy in case a huge amount of training data is provided. In this paper, a robust approach for license plate detection (LPD) based on YOLO v.3 is proposed which takes advantage of high detection accuracy and real-time performance. The mentioned approach can detect the license plate location of vehicles as a general representation of vehicle presence in images. To train the model, a dataset of vehicle images with Iranian license plates has been generated by the authors and augmented to provide a wider range of data for test and train purposes. It should be mentioned that the proposed method can detect the license plate area as an indicator of vehicle presence with no Optical Character Recognition (OCR) algorithm to distinguish characters inside the license plate. Experimental results have shown the high performance of the system with a precision 0.979 and recall 0.972.

中文翻译：

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基于深度学习方法的准确实时车牌检测方法

在摄像头发挥重要作用的视觉驱动智能交通系统 (ITS) 中，准确检测和重新识别车辆是基本要求。因此，最近的方法采用了广泛的算法来提供尽可能好的精度。这些方法通常基于其视觉外观特征（例如车牌、前灯或其他一些可区分的规格）生成车辆检测模型。在不同的目标检测方法中，深度神经网络 (DNN) 在提供大量训练数据的情况下具有出色的检测精度优势。在本文中，提出了一种基于 YOLO v.3 的鲁棒车牌检测 (LPD) 方法，该方法利用了高检测精度和实时性能。上述方法可以检测车辆的车牌位置，作为图像中车辆存在的一般表示。为了训练模型，作者生成了带有伊朗车牌的车辆图像数据集，并对其进行了扩充，以提供更广泛的数据用于测试和训练目的。应该提到的是，所提出的方法可以检测车牌区域作为车辆存在的指标，而无需光学字符识别（OCR）算法来区分车牌内的字符。实验结果表明该系统具有高性能，精度为 0.979，召回率为 0.972。作者生成了带有伊朗车牌的车辆图像数据集，并对其进行了扩充，以提供更广泛的数据用于测试和训练目的。应该提到的是，所提出的方法可以检测车牌区域作为车辆存在的指标，而无需光学字符识别（OCR）算法来区分车牌内的字符。实验结果表明该系统具有高性能，精度为 0.979，召回率为 0.972。作者生成了带有伊朗车牌的车辆图像数据集，并对其进行了扩充，以提供更广泛的数据用于测试和训练目的。应该提到的是，所提出的方法可以检测车牌区域作为车辆存在的指标，而无需光学字符识别（OCR）算法来区分车牌内的字符。实验结果表明该系统具有高性能，精度为 0.979，召回率为 0.972。