Synthetizing Virtual Construction Images to Strengthen Real Data Volume and Variety in Real-world Application Scenarios

Abstract

Despite the potential of synthetic construction images, it remains unknown whether they can strengthen real-data volume and variety in real-world scenarios, wherein a given, real training dataset is small and biased, or large but biased. To address this, we synthetize artificial images in a computer environment to strengthen a real training dataset and test its supplementary effects in both scenarios. Specifically, we simulate a worker’s physical behaviors, capture 2D synthetic images, and annotate its bounding box using a 3D-2D projection algorithm. After combining these synthetic images with a real dataset, we train a vision-based worker detection model and evaluate its performance in each scenario. Results show that the model’s performance is improved by up to 59.1% and 12.8% in each scenario, respectively, comparing to only adopting real images. This indicates that synthetic images can enrich the restricted volume and variety of a given, real training dataset in field application scenarios.