Data debiased traffic sign recognition using MSERs and CNN

This paper proposes a traffic sign recognition algorithm which is unaffected by dataset bias. Color information is an important element in traffic sign recognition, the performance of which can be affected by weather conditions, illumination, and the use of different cameras. In order to overcome this problem, our approach involves traffic sign detection and classification. In a detection module, red and blue color enhancement with MSERs is performed to improve the extraction of candidate regions of traffic signs. A Bayesian classifier with a DtB feature is used to detect traffic signs. Detected traffic signs are classified via spatial transformer networks based on convolutional neural networks. In public datasets, this work is evaluated with the results obtained featuring competitive accuracy without a training dataset.