Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network
Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network
复制标题
使用多任务卷积神经网络进行交通标志识别
DOI：
10.1109/tits.2017.2714691
复制
发表时间：
2018-04-01
影响因子：
8.5
通讯作者：
Fan, Bin
中科院分区：
文献类型：
--
作者：
Luo, Hengliang;Yang, Yi;Fan, Bin
虽然交通标志识别的研究已有多年，但已有的工作大多集中在基于符号的交通标志识别上。本文提出了一种新的基于数据驱动的系统来识别安装在汽车上的摄像机拍摄的视频序列中的所有类型的交通标志，包括基于符号的标志和基于文本的标志。该系统包括交通标志感兴趣区域(ROI)提取、ROI细化与分类、ROI后处理三个阶段。首先利用灰度和归一化RGB通道上最稳定的极值区域从每一帧中提取交通标志感兴趣区。然后，通过提出的多任务卷积神经网络对它们进行细化并分配到它们的详细类中，该网络使用大量的数据进行训练，包括合成的交通标志和街景标记的图像。后处理最后将所有帧的结果组合在一起，做出识别决策。实验结果证明了该系统的有效性。
Although traffic sign recognition has been studied for many years, most existing works are focused on the symbol-based traffic signs. This paper proposes a new data-driven system to recognize all categories of traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post-processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremal regions on gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi-task convolutional neural network, which is trained with a large amount of data, including synthetic traffic signs and images labeled from street views. The post-processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system.