Large-Scale Traffic-Sign Detection and Recognition

We developed a Mask-RCNN-based method for large-scale traffic sign detection. The detector is trained on the proposed DFG dataset with over 7000 images of traffic signs. The dataset consists of 200 traffic sign categories of different interclass variabilty. Accurate detection and localization is achieved with the additional offline tracking of the detection forward and backward in time.