--- annotations_creators: [] language: en license: cc-by-4.0 size_categories: - 1K - **Repository:** https://github.com/phiyodr/dacl10k-toolkit - **Paper:** https://arxiv.org/abs/2309.00460 - **Demo:** https://try.fiftyone.ai/datasets/dacl10k/samples - **Homepage:** https://dacl.ai/workshop.html ## Uses - identifying reinforced concrete defects - informing restoration works, traffic load limitations or bridge closures [More Information Needed] ## Dataset Structure The dacl10k dataset includes images collected during concrete bridge inspections acquired from databases at authorities and engineering offices, thus, it represents real-world scenarios. Concrete bridges represent the most common building type, besides steel, steel composite, and wooden bridges. dacl10k distinguishes 13 bridge defects as well as 6 bridge components that play a key role in the building assessment. Based on the assessment, actions (e.g., restoration works, traffic load limitations, and bridge closures) are determined. The inspection itself and the resulting actions often impede the traffic and thus private persons and the economy. Furthermore, an ideal timing for restoration helps achieving long-term value added and can save a lot of money. It is important to note that dacl10k includes images from bridge inspections but is not restricted to this building type. Classes of the concrete and general defect group in dacl10k can appear on any building made of concrete. Therefore, it is relevant for most of the other civil engineering structures, too. ## Citation **BibTeX:** ```bibtex @misc{flotzinger2023dacl10k, title={dacl10k: Benchmark for Semantic Bridge Damage Segmentation}, author={Johannes Flotzinger and Philipp J. Rösch and Thomas Braml}, year={2023}, eprint={2309.00460}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Dataset Card Authors [Jacob Marks](https://huggingface.co/jamarks)