  Infrastructure managers must maintain high standards to ensure user
satisfaction during the lifecycle of infrastructures. Surveillance cameras and
visual inspections have enabled progress in automating the detection of
anomalous features and assessing the occurrence of deterioration. However,
collecting damage data is typically time consuming and requires repeated
inspections. The one-class damage detection approach has an advantage in that
normal images can be used to optimize model parameters. Additionally, visual
evaluation of heatmaps enables us to understand localized anomalous features.
The authors highlight damage vision applications utilized in the robust
property and localized damage explainability. First, we propose a civil-purpose
application for automating one-class damage detection reproducing a fully
convolutional data description (FCDD) as a baseline model. We have obtained
accurate and explainable results demonstrating experimental studies on concrete
damage and steel corrosion in civil engineering. Additionally, to develop a
more robust application, we applied our method to another outdoor domain that
contains complex and noisy backgrounds using natural disaster datasets
collected using various devices. Furthermore, we propose a valuable solution of
deeper FCDDs focusing on other powerful backbones to improve the performance of
damage detection and implement ablation studies on disaster datasets. The key
results indicate that the deeper FCDDs outperformed the baseline FCDD on
datasets representing natural disaster damage caused by hurricanes, typhoons,
earthquakes, and four-event disasters.
