Datasets Comparison

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.

Structural Health Monitoring

Creative Commons Attribution 4.0 International

Concrete structures deteriorate over time due to environmental exposure and mechanical stress, leading to various types of damage such as cracking, spalling, corrosion, and exposed rebar. Automated detection using deep learning-based computer vision techniques is limited by the lack of high-quality, annotated datasets. To address this challenge, this paper presents MDMCS (Multi-Damage Monitoring of Concrete Structures), a dataset of 1,200 images with precise pixel-wise annotations involving four types of damage (cracking, spalling, corrosion, and exposed rebar) and diverse lighting conditions and material textures. The dataset has been evaluated using six state-of-the-art segmentation models, validating the efficacy of the dataset and providing benchmarks for damage detection models. MDMCS will facilitate advances in artificial intelligence-powered structural monitoring and robot-assisted automatic inspection for improving the operation and maintenance of concrete structures.

Structural Health Monitoring

Creative Commons Attribution 4.0 International