HRCDS: A Benchmark Dataset for High-Resolution Concrete Damage Segmentation
Description
Concrete structures deteriorate due to environmental stressors, aging, and mechanical loads, resulting in cracks, spalling, and corrosion. Early damage detection is essential for ensuring structural integrity and safety. While deep learning has improved automated detection, its effectiveness is constrained by the lack of high-quality datasets with diverse damage types and precise annotations. Existing datasets often suffer from low resolution, limited variability, and inadequate labeling, hindering model generalization. To overcome these challenges, a high-resolution concrete damage segmentation dataset (HRCDS) has been introduced for deep learning applications in structural health monitoring. HRCDS offers pixel-wise annotations for various damage types, including cracks, exposed rebar, corrosion strain, and surface spalling, captured under different lighting conditions and textures. The public release of HRCDS aims to drive advancements in AI-powered structural assessment, fostering innovation in civil engineering, deep learning, and digital twin technologies.