  Visual inspection is predominantly used to evaluate the state of civil
structures, but recent developments in unmanned aerial vehicles (UAVs) and
artificial intelligence have increased the speed, safety, and reliability of
the inspection process. In this study, we develop a semantic segmentation
network based on vision transformers and Laplacian pyramids scaling networks
for efficiently parsing high-resolution visual inspection images. The massive
amounts of collected high-resolution images during inspections can slow down
the investigation efforts. And while there have been extensive studies
dedicated to the use of deep learning models for damage segmentation,
processing high-resolution visual data can pose major computational
difficulties. Traditionally, images are either uniformly downsampled or
partitioned to cope with computational demands. However, the input is at risk
of losing local fine details, such as thin cracks, or global contextual
information. Inspired by super-resolution architectures, our vision transformer
model learns to resize high-resolution images and masks to retain both the
valuable local features and the global semantics without sacrificing
computational efficiency. The proposed framework has been evaluated through
comprehensive experiments on a dataset of bridge inspection report images using
multiple metrics for pixel-wise materials detection.
