HRCDS: A Benchmark Dataset for High-Resolution Concrete Damage Segmentation

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.

混凝土结构会因环境应力、老化及机械荷载作用发生劣化，进而产生裂缝、表面剥落与钢筋锈蚀等病害。早期病害检测对保障结构完整性与安全性至关重要。尽管深度学习技术推动了自动化结构检测的发展，但由于缺乏涵盖多样病害类型且标注精准的高质量数据集，其实际应用效果仍受到制约。现有数据集普遍存在分辨率偏低、样本多样性不足、标注质量欠佳等问题，严重阻碍了模型泛化能力的提升。为应对上述挑战，研究人员推出了一款面向结构健康监测深度学习应用的高分辨率混凝土损伤分割数据集（HRCDS）。该数据集针对裂缝、裸露钢筋、锈蚀应变以及表面剥落等多种病害类型提供像素级标注，样本采集于不同光照条件与纹理背景下。HRCDS的公开发布旨在推动人工智能驱动的结构评估技术进步，助力土木工程、深度学习以及数字孪生等领域的技术创新。