论文标题
基于深度可分离卷积网络的端到端高精度的车牌识别
End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks
论文作者
论文摘要
自动车牌识别在现代运输系统中起着至关重要的作用，例如交通监控和违规侦查。在实际情况下，车牌识别仍然面临许多挑战，并且受到不可预测的干扰（例如天气或照明条件）的损害。近年来，已经提出了许多基于机器学习的ALPR解决方案来解决此类挑战。但是，大多数人都不令人信服，要么是因为它们的结果是在缺乏各种环境的小型或简单的数据集上评估的，要么是因为它们需要强大的硬件才能在现实世界应用中获得合理的框架。在本文中，我们提出了一个新颖的无分段框架，以识别车牌识别并引入NP-ALPR，NP-ALPR是一个类似于真实世界的各种情况。提出的网络模型由最新的深度学习方法和最先进的想法组成，并从新颖的网络体系结构中受益。与以前的工作相比，它具有较低的计算要求，其精度更高。我们在三个不同的数据集上评估了所提出的方法的有效性，并显示了超过99％和70 fps的识别精度，这表明我们的方法不仅是稳健的，而且在计算上也有效。
Automatic license plate recognition plays a crucial role in modern transportation systems such as for traffic monitoring and vehicle violation detection. In real-world scenarios, license plate recognition still faces many challenges and is impaired by unpredictable interference such as weather or lighting conditions. Many machine learning based ALPR solutions have been proposed to solve such challenges in recent years. However, most are not convincing, either because their results are evaluated on small or simple datasets that lack diverse surroundings, or because they require powerful hardware to achieve a reasonable frames-per-second in real-world applications. In this paper, we propose a novel segmentation-free framework for license plate recognition and introduce NP-ALPR, a diverse and challenging dataset which resembles real-world scenarios. The proposed network model consists of the latest deep learning methods and state-of-the-art ideas, and benefits from a novel network architecture. It achieves higher accuracy with lower computational requirements than previous works. We evaluate the effectiveness of the proposed method on three different datasets and show a recognition accuracy of over 99% and over 70 fps, demonstrating that our method is not only robust but also computationally efficient.
