Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10016
• 期：1
• 页码：205-216
• 全文大小：1,896 KB
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• 作者单位：Ali Youssef (18)
  Dario Albani (18)
  Daniele Nardi (18)
  Domenico Daniele Bloisi (18)
  
  18. Department of Computer, Control, and Management Engineering, Sapienza University of Rome, via Ariosto 25, 00185, Rome, Italy
  
• 丛书名：Advanced Concepts for Intelligent Vision Systems
• ISBN：978-3-319-48680-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10016

文摘

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.