dacl10k: Benchmark for Semantic Bridge Damage Segmentation
This provides a benchmark for researchers and practitioners in bridge inspection to improve damage detection, though it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the problem of limited datasets for reinforced concrete defect recognition by introducing dacl10k, a diverse dataset of 9,920 images for multi-label semantic segmentation, with the best baseline model achieving a mean intersection-over-union of 0.42 on the test set.
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.