Deep learning has become a standard approach to machine vision in recent years. Despite several advances, it requires large amounts of annotated data. Nonetheless, in many applications, large-scale data acquisition and annotation is expensive and data imbalance is an intrinsic problem. To address these challenges, we propose a novel synthetic database generation method that only requires (i) arbitrary natural images, i.e., does not demand real images from the target domain, and (ii) templates of the traffic signs. Our method does not aim at overcoming the training with real data but to be a compatible option when there is a lack of real data. Results with data of multiple countries show that the synthetic database generated without human effort is effective for training a deep traffic sign detector. On large datasets, training with a fully synthetic dataset almost matches the performance of training with a real one. When compared to training with a smaller dataset of real images, training with synthetic images increased the accuracy by 12.25%. The proposed method also improves the performance of the detector when target-domain data are available.

近年来，深度学习已成为机器视觉的标准方法。尽管取得了一些进步，但它需要大量的注释数据。尽管如此，在许多应用中，大规模数据采集和注释成本高昂，数据不平衡是一个内在问题。为了应对这些挑战，我们提出了一种新的合成数据库生成方法，它只需要（i）任意自然图像，即不需要来自目标域的真实图像，以及（ii）交通标志模板。我们的方法并非旨在克服使用真实数据进行的训练，而是在缺乏真实数据时成为一种兼容的选择。多个国家数据的结果表明，无需人工生成的合成数据库对于训练深度交通标志检测器是有效的。在大型数据集上，使用完全合成的数据集进行训练几乎可以与真实数据集的训练性能相匹配。与使用较小的真实图像数据集进行训练相比，使用合成图像进行训练将准确率提高了 12.25%。当目标域数据可用时，所提出的方法还提高了检测器的性能。