Traffic Sign Detection Using R-CNN

  • Philipp RehlaenderEmail author
  • Maik Schroeer
  • Gavneet Chadha
  • Andreas Schwung
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)


Due to the increasing popularity of driving assistance systems, traffic sign detection has gained increased attention due to its role in traffic safety. This paper presents a traffic sign detection algorithm based on the regions with convolutional neural network (R-CNN) method. In this method, a segmentation algorithm proposes regions likely to contain a traffic sign. These regions are analyzed by a convolutional neural network providing a feature vector to a support vector machine. This method, however, suffers from a high computation time due to an excessive amount of regions proposed. To reduce the number of regions, this work proposes a perspective based logic that discards regions unlikely to contain traffic signs because of a size-position-mismatch. This results in an average 93% reduction of proposed regions. For faster training, the system consists of a two-layer support vector machine. The first layer is used to categorize the family of the picture, the second set of SVMs for classifying the traffic sign. The overall classification accuracy of this technique is 86%. Feeding the results to a tracking algorithm may result in an overall improvement because frames would compensate errors.


Object detection Convolutional neural network Regions with convolutional neural networks (R-CNN) Support vector machines (SVM) 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Philipp Rehlaender
    • 1
    Email author
  • Maik Schroeer
    • 2
  • Gavneet Chadha
    • 3
  • Andreas Schwung
    • 3
  1. 1.Paderborn UniversityPaderbornGermany
  2. 2.Busch-Jaeger Elektro GmbHLüdenscheidGermany
  3. 3.South Westphalia University of Applied SciencesIserlohnGermany

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