dacl10k: Benchmark for Semantic Bridge Damage Segmentation

TL;DR

The paper introduces dacl10k, a large and diverse dataset with nearly 10,000 images for multi-label semantic segmentation of reinforced concrete defects, aiming to improve bridge inspection and maintenance.

Contribution

It presents the dacl10k dataset, the largest of its kind for RCDs, including 12 damage and 6 component classes, and evaluates baseline models on this dataset.

Findings

Best model achieves 0.42 mean IoU.

dacl10k is the largest dataset for this domain.

Baseline models provide a starting point for future research.

Abstract

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.