Context-aware Training Image Synthesis for Traffic Sign Recognition
Akira Sekizawa, Katsuto Nakajima · 2019
In this paper, we propose a method for training traffic sign detectors without using actual images of the traffic signs. The method involves using training images of road scenes that were synthetically generated to train a deep-learning based end-to-end traffic sign detector (which includes detection and classification). Conventional methods for generating training data mostly focus only on producing small images of the traffic sign alone and cannot be used for generating images for training end-to-end traffic sign detectors, which use images of the overall scenes as the training data. In this paper, we propose a method for synthetically generating road scenes to use as the training data for end-to-end traffic sign detectors. We also show that considering the context information of the surroundings of the traffic signs when generating scenes is effective for improving the precision.