Towards Real-Time Traffic Sign Detection and Classification
Towards Real-Time Traffic Sign Detection and Classification
复制标题
实现实时交通标志检测和分类
DOI：
10.1109/tits.2015.2482461
复制
发表时间：
2016-07-01
影响因子：
8.5
通讯作者：
Wu, Fuchao
中科院分区：
文献类型：
--
作者：
Yang, Yi;Luo, Hengliang;Wu, Fuchao
交通标志识别在驾驶员辅助系统和智能自动驾驶汽车中起着重要的作用。它的实时性能是非常可取的，除了它的识别性能。本文的目的是处理实时交通标志识别，以快速处理时间定位什么类型的交通标志出现在输入图像的哪个区域中。为了实现这一目标，我们首先提出了一个非常快速的检测模块，它比现有的最佳检测模块快20倍。我们的检测模块是基于交通标志的建议提取和分类建立在一个颜色概率模型和颜色HOG。然后，我们从卷积神经网络中收获，以进一步将检测到的信号分类到每个超类中的子类中。在德国和中国道路上的实验结果表明，我们的检测和分类方法都达到了与最先进的方法相当的性能，显着提高了计算效率。
Traffic sign recognition plays an important role in driver assistant systems and intelligent autonomous vehicles. Its real-time performance is highly desirable in addition to its recognition performance. This paper aims to deal with real-time traffic sign recognition, i.e., localizing what type of traffic sign appears in which area of an input image at a fast processing time. To achieve this goal, we first propose an extremely fast detection module, which is 20 times faster than the existing best detection module. Our detection module is based on traffic sign proposal extraction and classification built upon a color probability model and a color HOG. Then, we harvest from a convolutional neural network to further classify the detected signs into their subclasses within each superclass. Experimental results on both German and Chinese roads show that both our detection and classification methods achieve comparable performance with the state-of-the-art methods, with significantly improved computational efficiency.