Recognising reinforced concrete defects (RCDs) is a crucial element for
determining the structural integrity, traffic safety and durability of bridges.
However, most of the existing datasets in the RCD domain are derived from a
small number of bridges acquired in specific camera poses, lighting conditions
and with fixed hardware. These limitations question the usability of models
trained on such open-source data in real-world scenarios. We address this
problem by testing such models on our "dacl1k" dataset, a highly diverse RCD
dataset for multi-label classification based on building inspections including
1,474 images. Thereby, we trained the models on different combinations of
open-source data (meta datasets) which were subsequently evaluated both
extrinsically and intrinsically. During extrinsic evaluation, we report metrics
on dacl1k and the meta datasets. The performance analysis on dacl1k shows
practical usability of the meta data, where the best model shows an Exact Match
Ratio of 32%. Additionally, we conduct an intrinsic evaluation by clustering
the bottleneck features of the best model derived from the extrinsic evaluation
in order to find out, if the model has learned distinguishing datasets or the
classes (RCDs) which is the aspired goal. The dacl1k dataset and our trained
models will be made publicly available, enabling researchers and practitioners
to put their models to the real-world test.

识别钢筋混凝土缺陷（RCDs）对于确定桥梁的结构完整性、交通安全性和耐久性至关重要。本研究通过对多标签分类的建筑检查图像进行训练，提供了高度多样的 RCD 数据集 “dacl1k”，并谨慎测试现有模型在该数据集上的可用性，结果表明最佳模型的准确匹配率为 32%。

dacl1k：现实世界桥梁损伤数据集的开源数据测试

dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the  Test

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。