A Structure-Aware Deep Learning Framework for Automated Bridge Inspection Integrating SegFormer-Based Structural Member Segmentation and YOLOv8 Damage Detection

Aging bridge infrastructure and limited inspection resources have created an urgent need for automated and reliable bridge condition assessment systems. Most existing deep learning-based inspection approaches detect damage types from images without considering the structural member on which the damage occurs, limiting their practical utility for maintenance decision-making. This study proposes a structure-aware deep learning framework for automated bridge inspection that integrates structural member segmentation, multiclass damage detection, and spatial damage-to-member association within a unified pipeline. A SegFormer-based semantic segmentation model was trained on a custom bridge inspection dataset comprising 1,339 images to identify three primary structural member classes — main girder, deck slab, and abutment — achieving a test mean Intersection over Union (mIoU) of 0.851. Boundary refinement using the Segment Anything Model (SAM) in mask-prompt mode was applied to improve mask precision during training data preparation. A YOLOv8s object detection model was trained on a custom bridge damage dataset of 6,531 images to detect two damage classes — crack and corrosion — achieving a mean Average Precision (mAP50) of 0.445 at a confidence threshold of 0.30. The framework associates detected damage with segmented structural members using a region-based spatial assignment strategy, enabling structure-aware outputs such as “crack on main girder” and “corrosion on deck slab.” Manual evaluation on 100 bridge inspection images demonstrated a damage detection accuracy of 70.0% fully correct and 84.0% fully or partially correct, and a member assignment accuracy of 62.0% fully correct and 87.0% fully or partially correct. The main girder class achieved the highest combined accuracy for both damage detection (90.9%) and member assignment (93.9%). These results demonstrate the potential of the proposed framework for practical automated bridge inspection and infrastructure monitoring applications.