dCrack: Enhancing Fine-Grained Crack Segmentation with Edge-Guided Attention

Crack segmentation is vital for structural health monitoring, ensuring infrastructure maintenance such as roads, bridges, and buildings. In these scenarios, every improvement in accuracy and every second saved in processing time can make a significant difference in real-time monitoring, potentially preventing catastrophic failures and saving lives. However, cracks' irregularity, thinness, and low contrast make segmentation challenging, particularly as minority-class features. We present dCrack++, a deep learning architecture incorporating the proposed Edge-Guided Attention (EGA), specifically designed to enhance the detection of thin and irregular crack features across multiple scales, thus significantly improving segmentation accuracy while maintaining computational efficiency. To support model development, we curated dCrack61k, a comprehensive dataset comprising over 61 K images to address annotation inconsistencies, dataset overlap, and minority-class under-representation. Our experimental results demonstrate that dCrack++ consistently outperforms SOTA models in segmenting minority-class features across various surface types and conditions. Moreover, cross-domain evaluations on the CHASE_DB1 dataset for retinal vessel segmentation confirm dCrack++'s adaptability to similar tasks involving elongated structures, underscoring its robustness and generalizability to real-world applications.