Traffic Sign Recognition (TSR) is a crucial component in many automotive applications, such as driver assistance, sign maintenance, and vehicle autonomy. In this paper, we present an efficient approach to training a machine learning-based TSR solution. In our choice of recognition method, we have opted for convolutional neural networks, which have demonstrated best-in-class performance in previous works on TSR. One of the challenges related to training deep neural networks is the requirement for a large amount of training data. To circumvent the tedious process of acquiring and manually labelling real data, we investigate the use of synthetically generated images. Our networks, trained on only synthetic data, are capable of recognising traffic signs in challenging real-world footage. The classification results achieved on the GTSRB benchmark are seen to outperform existing state-of-the-art solutions.

中文翻译：

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使用合成数据训练方法的交通标志识别

交通标志识别 (TSR) 是许多汽车应用中的关键组件，例如驾驶员辅助、标志维护和车辆自动驾驶。在本文中，我们提出了一种有效的方法来训练基于机器学习的 TSR 解决方案。在我们选择识别方法时，我们选择了卷积神经网络，该网络在之前的 TSR 工作中表现出一流的性能。与训练深度神经网络相关的挑战之一是需要大量的训练数据。为了避免获取和手动标记真实数据的繁琐过程，我们研究了合成生成图像的使用。我们的网络仅使用合成数据进行训练，能够识别具有挑战性的现实世界镜头中的交通标志。