A scalable UAV-based structural health monitoring framework using augmented deep learning for multilevel damage classification

Key Points

• The framework achieves accurate damage classification, indicating high reliability for structural assessments.
• Key evidence shows over 90% accuracy in classifying damage levels using augmented deep learning techniques.
• Evaluation using UAV technology allows for scalable structural health monitoring across various environments.
• Significance highlights the potential for improving infrastructure safety and maintenance through advanced monitoring solutions.