Panoramic Vision–Driven Deep Learning Framework for Structural Inspection and Condition Assessment

Traditional structural inspection methods, while effective, often fall short in terms of efficiency, coverage, and consistency, thereby highlighting the need for innovative approaches. This paper presents a novel vision-based framework for structural inspection, condition assessment, and maintenance using panoramic views. Through advanced computer vision and deep learning techniques, this study presents a novel panoramic vision–driven framework that integrates image stitching, dual damage-structural surface segmentation, and code-compliant condition assessment into a unified workflow. Unlike conventional methods limited to localized damage detection, the proposed approach reconstructs defects within a global structural coordinate system, enabling context-aware damage localization and maintenance prioritization based on damage severity relative to total surface area. Case studies on concrete bridges demonstrate the framework’s ability to map multiple damages to specific components, with accuracy exceeding 90% for common damage types, directly informing code-based maintenance recommendations. This workflow bridges the gap between deep learning outputs and actionable engineering decisions, offering a scalable solution for infrastructure networks.