Damage Classification

Damage classification research focuses on automatically identifying and quantifying damage in various structures, from buildings and bridges to aircraft wings and wind turbines, using advanced computational methods. Current efforts leverage deep learning models, including variations of YOLO and Swin Transformers, often incorporating multi-modal data (e.g., imagery and sensor data) and 3D vision techniques for improved accuracy and robustness. These advancements aim to improve efficiency and safety in structural health monitoring, enabling faster damage assessment and more effective preventative maintenance in critical infrastructure.