MDMCS: A Benchmark Dataset for Multi-Damage Monitoring of Concrete Structures

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.