Papers › dacl10k: Benchmark for Semantic Bridge Damage Segmentation
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.
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Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | dacl10k v1 testdev | FPN EfficientNet-B4 w/ Aux loss | mIoU | 0.414 | #1 of 3 | Archive leaderboard | report |
| Semantic Segmentation | dacl10k v1 testdev | DeepLabv3+ EfficientNet-B4 | mIoU | 0.411 | #2 of 3 | Archive leaderboard | report |
| Semantic Segmentation | dacl10k v1 testdev | SegFormer mit-b1 | mIoU | 0.40 | #3 of 3 | Archive leaderboard | report |
| Semantic Segmentation | dacl10k v1 testfinal | FPN EfficientNet-B4 | mIoU | 42.4 | #1 of 1 | Archive leaderboard | report |
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Methods
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