Reliably identifying reinforced concrete defects (RCDs)plays a crucial role
in assessing the structural integrity, traffic safety, and long-term durability
of concrete bridges, which represent the most common bridge type worldwide.
Nevertheless, available datasets for the recognition of RCDs are small in terms
of size and class variety, which questions their usability in real-world
scenarios and their role as a benchmark. Our contribution to this problem is
"dacl10k", an exceptionally diverse RCD dataset for multi-label semantic
segmentation comprising 9,920 images deriving from real-world bridge
inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge
components that play a key role in the building assessment and recommending
actions, such as restoration works, traffic load limitations or bridge
closures. In addition, we examine baseline models for dacl10k which are
subsequently evaluated. The best model achieves a mean intersection-over-union
of 0.42 on the test set. dacl10k, along with our baselines, will be openly
accessible to researchers and practitioners, representing the currently biggest
dataset regarding number of images and class diversity for semantic
segmentation in the bridge inspection domain.

识别钢筋混凝土缺陷在评估混凝土桥梁的结构完整性、交通安全和长期耐久性方面起着至关重要的作用。本研究提出了一个名为 'dacl10k' 的多标签语义分割数据集，包括 9,920 张来自实际桥梁检查的图像，区分了 12 个损坏类别和 6 个关键桥梁组件，并提供了基线模型进行评估。最佳模型在测试集上达到了 0.42 的平均交叉并集。'dacl10k' 数据集以及我们的基线模型将对研究人员和实践者开放，代表了目前在桥梁检查领域中图像数量和类别多样性方面最大的数据集。