MDMCS

Abstract

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 data sets. To address this challenge, this paper presents multidamage monitoring of concrete structures (MDMCS), a data set of 1,200 images with precise pixelwise annotations involving four types of damage (cracking, spalling, corrosion, and exposed rebar) and diverse lighting conditions and material textures. The data set was evaluated using six state-of-the-art segmentation models, validating the efficacy of the data set 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.