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

A Convolutional Cost-Sensitive Crack Localization Algorithm for Automated and Reliable RC Bridge Inspection