Scene restoration from scaffold occlusion using deep learning-based
  methods
Yuexiong Ding Muyang Liu Xiaowei Luo
The occlusion issues of computer vision (CV) applications in construction
have attracted significant attention, especially those caused by the
wide-coverage, crisscrossed, and immovable scaffold. Intuitively, removing the
scaffold and restoring the occluded visual information can provide CV agents
with clearer site views and thus help them better understand the construction
scenes. Therefore, this study proposes a novel two-step method combining
pixel-level segmentation and image inpainting for restoring construction scenes
from scaffold occlusion. A low-cost data synthesis method based only on
unlabeled data is developed to address the shortage dilemma of labeled data.
Experiments on the synthesized test data show that the proposed method achieves
performances of 92% mean intersection over union (MIoU) for scaffold
segmentation and over 82% structural similarity (SSIM) for scene restoration
from scaffold occlusion.