Corroding reinforcement leads to cross section loss and reduced structural capacity of concrete bridges. Detecting exposed rebars (ER) is crucial during bridge inspection to plan countermeasures early and prevent further corrosion. With advancements in deep learning, several public datasets derived from inspection imagery have been released to identify ER and other concrete damage automatically. At the same time, Uncrewed Aerial Systems (UAS) have become more capable of navigating even underneath the bridge deck. This combination holds promise to improve efficiency of bridge inspection methods, but obtained imagery differs from available datasets, featuring very small damages and complex backgrounds. To address this mismatch, this work reviews publicly available ER datasets, presents a UAS-based bridge inspection dataset for evaluating ER damage (UBID-ER-val), and quantifies similarities and differences between them. We train several YOLOv8 models on conventional inspection documentation images and benchmark the reviewed datasets, scoring <strong>F2 = 0.229</strong> at <strong>S2DS</strong>, <strong>F2 = 0.430</strong> at <strong>CODEBRIM</strong>, <strong>F2 = 0.584</strong> at <strong>Dacl10k</strong>, compared to <strong>F2 = 0.505</strong> at <strong>UBID-ER-val</strong>. We analyse factors influencing performance and find that tiled inference raises Recall (+0.166) but drastically reduces Precision (−0.309), while matching training and validation image resolution underperforms across all datasets (−0.061 to −0.129). The differences in best-performing combinations underscore the underlying domain shift that complicates practical deployment. As a practical outcome of this work, UBID-ER-val is made publicly available to enable objective benchmarking of ER detection models and to assess their reliability under field conditions.