Moving-feature-driven label propagation for training data generation from target domains

Deep learning models often suffer from performance degradation when applied to construction sites that differ from the source domain due to their sensitivity to data distribution shifts. Although methods such as transfer learning, domain adaptation, and synthetic data generation have been explored to improve generalization, collecting and annotating data from new target domains remains a labor-intensive bottleneck. This study presents a self-training-based framework to generate training data for construction object detection in unlabeled target domains. The method identifies moving objects using optical flow estimation, propagates class labels through iterative self-training, and synthesizes realistic training images via image inpainting and copy-paste augmentation. Experimental results from four visually distinct construction scenes demonstrate that the proposed method significantly improves detection performance without relying on manually labeled target data. These findings contribute to advancing automated and scalable domain adaptation techniques for vision-based construction monitoring.