dacl10k: Benchmark for Semantic Bridge Damage Segmentation
Johannes Flotzinger Philipp J. Rösch Thomas Braml
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.