Learning to Detect Traffic Signs: Comparative Evaluation of Synthetic and Real-World Datasets

This study compares the performance of sign detection based on synthetic training data to the performance of detection based on real-world training images. Viola-Jones detectors are created for 4 different traffic signs with both synthetic and real data, and varying numbers of training samples. The detectors are tested and compared. The result is that while others have successfully used synthetic training data in a classification context, it does not seem to be a good solution for detection. Even when the synthetic data covers a large part of the parameter space, it still performs significantly worse than real-world data.