Data-Efficient and Context-Aware Deep Learning Models for Crack Analysis: From Robust Detection to Precise Segmentation
Abstract: Cracks are among the earliest and most critical indicators of structural deterioration in civil infrastructure, including road pavements, concrete bridges, and buildings, and their timely detection and precise delineation are essential for ensuring safety, prioritizing maintenance, and reducing long-term repair costs. Yet, automated crack analysis remains a fundamentally challenging computer vision (CV) problem. Cracks are typically thin, irregular, and fragmented, often embedded within complex backgrounds and affected by illumination changes, surface texture variations, shadows, and noise. Moreover, acquiring dense pixel-level annotations for segmentation is labor-intensive and costly, limiting the scalability of fully supervised approaches. While machine learning (ML) and, more recently, deep learning (DL) methods have significantly advanced automated inspection, many existing models still struggle to handle complex real-world scenes, achieve robust cross-dataset generalization, and maintain data efficiency.
