Recognition of Traffic Signs with Artificial Neural Networks: A Novel Dataset and Algorithm

Creative Commons License

Kerim A., EFE M. Ö.

3rd International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC), South Korea, 13 - 16 April 2021, pp.171-176 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icaiic51459.2021.9415238
  • Country: South Korea
  • Page Numbers: pp.171-176
  • Keywords: artificial neural networks, image classification, pattern recognition, data preprocessing
  • Hacettepe University Affiliated: Yes


Traffic sign classification is a prime Issue for autonomous platform industries such as autonomous cars. Towards the goal of recognition, most recent classification methods deploy Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs). In this work, we provide a novel dataset and a hybrid ANN that achieves accurate results that are very close to the state-of-the-art ones. When training and testing on German Traffic Sign Recognition Benchmarks (GTSRB) a top-5 classification accuracy of 80% was achieved for 43 classes. On the other hand, a top-2 classification accuracy of 95% was reached on our novel dataset for 10 classes. This accomplishment can be linked to the fact that the proposed hybrid ANN combines 9 different models trained on color intensity, HOG (Histograms of Oriented Gradients) and LBP (Local Binary Pattern) features.