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DeepSign: Deep Learning based Traffic Sign Recognition

机译：DeepSign：基于深度学习的交通标志识别

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摘要

This paper investigates the traffic sign recognition task with deep learning methods. The proposed algorithm which is called DeepSign includes three modules: a detection module (PosNet) for locating the traffic sign in a static image, a classification module (PatchNet) for classifying the detected image patch, and a temporal filter for correcting the recognition results. The PosNet is a binary object detection convolution neural network which regards all traffic signs as one class and the background as the other class. Different from the traditional works which recognize the traffic sign on the static image, the proposed temporal filter exploits the contextual information to recover the missed detection region and correct the false classification. The experiments validate the effectiveness of the proposed algorithm. It achieved the third place on the traffic sign recognition task in 2017 China intelligent vehicle future challenge (2017 CIVFC).
机译：本文采用深度学习方法对交通标志识别任务进行了研究。所提出的称为DeepSign的算法包括三个模块：用于在静态图像中定位交通标志的检测模块（PosNet），用于对检测到的图像补丁进行分类的分类模块（PatchNet）和用于校正识别结果的时间滤波器。 PosNet是一个二进制对象检测卷积神经网络，它将所有交通标志视为一个类别，将背景视为另一类别。与在静态图像上识别交通标志的传统作品不同，所提出的时间滤波器利用上下文信息来恢复错过的检测区域并纠正错误的分类。实验验证了所提算法的有效性。在2017年中国智能车未来挑战赛（2017 CIVFC）中，它在交通标志识别任务中获得第三名。

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