Optimized training of deep neural network for image analysis using synthetic objects and augmented reality
Thomas T. Lu, Alexander Huyen, Luan Nguyen, Joseph Osborne, Sarah Eldin, Kyongsik Yun · 2019
Acquiring large amounts of data for training and testing Deep Learning (DL) models is time consuming and costly. The development of a process to generate synthetic objects and scenes using 3D graphics software is presented. By programming the path and environment in a 3D graphical engine, complex objects and scenes can be generated for the purpose of training and testing a Deep Neural Network (DNN) model in specific vision tasks. An automatic process has been developed to label and segment objects in synthetic images and generate their corresponding ground truth files. Performances of DNNs trained with synthetic data have been shown to outperform DNNs trained with real data.