Scene classification-assisted deep learning for crack detection of asphalt pavements in RC bridge

Addressing age-related deterioration of road bridges requires efficient crack detection methods to replace traditional visual inspections. This study proposes a deep learning-based approach for detecting pavement cracks in RC bridges using UAV-captured visible images. The method addresses the challenge of varying illumination conditions by implementing a two-stage process: (1) scene classification to distinguish noisy areas, sunlit pavement areas, and shaded pavement areas, and (2) specialized crack detection models trained for specific lighting conditions. U-Net architecture is employed for both scene classification and crack detection models. Image processing techniques are applied in L*a*b* color space to enhance model performance. The approach is validated on three in-service RC bridges. Results demonstrate high-precision scene classification and improved crack detection accuracy when using lighting condition-specific models. The highest detection accuracy is achieved when the lighting conditions used for model training match those of the target detection areas, confirming the effectiveness of condition-specific training approaches.