OmniCrack30k: A Benchmark for Crack Segmentation and the Reasonable Effectiveness of Transfer Learning

Christian Benz, Volker Rodehorst; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3876-3886

Large benchmarking datasets such as ImageNet COCO Cityscapes or ScanNet have enormously promoted research in computer vision. For the domain of crack segmentation no such large and well-maintained benchmark exists. Crack segmentation is characterized by the decentralized creation of datasets almost all of which have their specific right to existence. Each dataset covers a different aspect of the surprisingly complex landscape of materials acquisition conditions and appearances linked to crack segmentation. The OmniCrack30k dataset forms the first large-scale systematic and thorough approach to provide a sustainable basis for tracking methodical progress in the field of crack segmentation. It contains 30k samples from over 20 datasets summing up to 9 billion pixels in total. Featuring materials as diverse as asphalt ceramic concrete masonry and steel it paves the road towards universal crack segmentation a currently under-explored topic. Experiments indicate the effectiveness of transfer learning for crack segmentation: nnU-Net achieves a mean clIoU_4px of 64% outperforming all other approaches by at least 10% points.