Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 425-432, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-425-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 425-432, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-425-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS

A. Hanel, D. Kreuzpaintner, and U. Stilla A. Hanel et al.
  • Photogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, Germany

Keywords: Scene Understanding, Traffic Sign Detection, Machine Learning, Neural Network, Synthetic Images

Abstract. Recently, several synthetic image datasets of street scenes have been published. These datasets contain various traffic signs and can therefore be used to train and test machine learning-based traffic sign detectors. In this contribution, selected datasets are compared regarding ther applicability for traffic sign detection. The comparison covers the process to produce the synthetic images and addresses the virtual worlds, needed to produce the synthetic images, and their environmental conditions. The comparison covers variations in the appearance of traffic signs and the labeling strategies used for the datasets, as well. A deep learning traffic sign detector is trained with multiple training datasets with different ratios between synthetic and real training samples to evaluate the synthetic SYNTHIA dataset. A test of the detector on real samples only has shown that an overall accuracy and ROC AUC of more than 95 % can be achieved for both a small rate of synthetic samples and a large rate of synthetic samples in the training dataset.