  Bridges are an essential part of the transportation infrastructure and need
to be monitored periodically. Visual inspections by dedicated teams have been
one of the primary tools in structural health monitoring (SHM) of bridge
structures. However, such conventional methods have certain shortcomings.
Manual inspections may be challenging in harsh environments and are commonly
biased in nature. In the last decade, camera-equipped unmanned aerial vehicles
(UAVs) have been widely used for visual inspections; however, the task of
automatically extracting useful information from raw images is still
challenging. In this paper, a deep learning semantic segmentation framework is
proposed to automatically localize surface cracks. Due to the high imbalance of
crack and background classes in images, different strategies are investigated
to improve performance and reliability. The trained models are tested on
real-world crack images showing impressive robustness in terms of the metrics
defined by the concepts of precision and recall. These techniques can be used
in SHM of bridges to extract useful information from the unprocessed images
taken from UAVs.
