Traffic sign detection is one of the key components in autonomous driving. Advanced autonomous vehicles armed with high quality sensors capture high definition images for further analysis. Detecting traffic signs, moving vehicles, and lanes is important for localization and decision making. Traffic signs, especially those that are far from the camera, are small, and so are challenging to traditional object detection methods. In this work, in order to reduce computational cost and improve detection performance, we split the large input images into small blocks and then recognize traffic signs in the blocks using another detection module. Therefore, this paper proposes a three-stage traffic sign detector, which connects a BlockNet with an RPN–RCNN detection network. BlockNet, which is composed of a set of CNN layers, is capable of performing block-level foreground detection, making inferences in less than 1 ms. Then, the RPN–RCNN two-stage detector is used to identify traffic sign objects in each block; it is trained on a derived dataset named TT100KPatch. Experiments show that our framework can achieve both state-of-the-art accuracy and recall; its fastest detection speed is 102 fps.

中文翻译：

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大型全景交通标志的三阶段实时检测器

交通标志检测是自动驾驶的关键组成部分之一。配备有高质量传感器的先进自动驾驶汽车可捕获高清图像以进行进一步分析。检测交通标志，行驶中的车辆和车道对于本地化和决策很重要。交通标志，特别是那些远离摄像机的交通标志，很小，因此对传统的物体检测方法具有挑战性。在这项工作中，为了减少计算成本并提高检测性能，我们将较大的输入图像分割为小块，然后使用另一个检测模块识别这些块中的交通标志。因此，本文提出了一种三级交通标志检测器，它将BlockNet与RPN-RCNN检测网络连接起来。BlockNet由一组CNN层组成，能够执行块级前景检测，并在不到1 ms的时间内进行推断。然后，使用RPN-RCNN两级检测器来识别每个街区中的交通标志对象。在名为TT100KPatch的派生数据集上进行训练。实验表明，我们的框架可以实现最先进的准确性和召回率；其最快的检测速度为102 fps。