Dual-encoder Multi-Scale Refinement network for robust crack segmentation across diverse domains

Al-Sameai H., SALEH R. A. A., de Moura J., AKAY R.

Automation in Construction, cilt.188, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 188
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.autcon.2026.107004
  • Dergi Adı: Automation in Construction
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, ICONDA Bibliographic, INSPEC
  • Anahtar Kelimeler: Boundary refinement, CNN–ViT hybrid, Crack segmentation, Deep learning, Generalization performance, Infrastructure monitoring, Multi-scale feature fusion
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Accurate crack segmentation is critical for infrastructure monitoring but remains challenging due to diverse crack morphologies, complex backgrounds, and domain shifts. This paper proposes DMSRCrack, a Dual-encoder Multi-Scale Refinement network for robust crack segmentation across diverse domains. The proposed architecture integrates a hybrid CNN–ViT encoder for local and global feature extraction, a Crack Detail Enhancement Module (CDEM) for preserving thin crack structures, a Boundary Refinement Head (BRH) for contour sharpening, and a Multi-Scale Fusion (MSF) module for scale-consistent representation. Across eight benchmark datasets, DMSRCrack achieves an average Dice of 0.7949 ± 0.0655 and IoU of 0.6639 ± 0.0917 in dataset-wise training. Under leave-one-dataset-out evaluation, it attains the highest IoU on DeepCrack (0.4056), Rissbilder (0.3540), and Crack500 (0.3008). Ablation and computational analyses further confirm the effectiveness and practical efficiency of the proposed contributions.