Evaluation of a Traffic Sign Detector by Synthetic Image Data for Advanced Driver Assistance Systems

Abstract

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.

Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines

“…The CAD models are also used in robotic controls to help training the robot to get the pose of the target to grasp [9], [10]. There are also some works [7], [16], [8], [11], [12] proposed to create a totally virtual 3D environment, from which various data can be generated for different tasks including object detection, semantic segmentation, disparity estimation, etc. The other reason why the simulated data generated has less overlap with the real data in these works is that they targeted on tasks in more general scenes which may differ a lot among one another.…”