A two-phase framework to integrate image classification and instance segmentation for rapid railway viaduct damage assessment

Rapid and accurate damage assessment of railway viaducts is critical following an earthquake. This study proposes a novel two-phase framework for comprehensive damage assessment for post-earthquake reinforced concrete components in railway viaducts, which integrates image classification and instance segmentation. High-resolution images captured by cameras are first cropped or resized into standardized image blocks. In the first phase, a classification model categorizes these image blocks into background, concrete crack, and exposed rebar, enabling preliminary damage assessment of the concrete components. In the second phase, instance segmentation is applied only to the image blocks identified as containing concrete crack or exposed rebar to precisely locate the damage and achieve detailed assessment. By employing image classification to exclude non-damaged areas, the method significantly reduces the computational cost while maintaining high accuracy. The proposed method is validated using the Tokaido dataset of post-earthquake railway viaducts, demonstrating superior performance in both detection accuracy and processing efficiency compared to conventional methods.