复杂环境下基于角点回归的全卷积神经网络的车牌定位

车牌定位是车牌识别系统中核心部分，具有较高的研究和应用价值。尽管近些年来该研究取得了很大的进展，但仍无法很好地解决低亮度、低分辨率和车辆倾斜等环境下的定位问题。本文提出了一种新的全卷积神经网络，通过回归车牌角点的方式准确地进行车牌定位。为了保证训练的有效性，对45 000幅含有车牌的图像进行人工标注。同时，对标注的图像随机进行平移、缩放、旋转和加噪，提高训练样本的数量和多样性。在本文构建的卡口图像数据集和复杂环境数据集上与两种方法进行了比较，验证了本文方法的有效性。

Learning Corner Regression-based Fully Convolutional Neural Network for License Plate Localization in Complex Scene

License plate localization, the core component of license plate recognition system, is valuable in both academic development and potential applications. Though much progress has been made in recent years, challenging problems still exist in the complex scenes, such as low luminance, low resolution and inclination scence of vehicle. This paper proposes a novel fully convolutional neural network to localize license plates accurately by a corner regression algorithm. To guarantee effective training in the proposed model, 45 000 sample images are annotated by one person. Meanwhile, the annotated sample images are processed by four operators, including translating, scaling, rotating and noising, to increase the number and diversity of the training samples. Extensive experiments on the newly collected datasets, trafficmonitoring dataset and the complex scene dataset, demonstrate the effectiveness of the proposed method against other two license plate localization methods.