REAL-TIME CRACK DETECTION IN MATERIALS USING A NOVEL CRACK-AWARE CNN-VIT HYBRID MODEL

Abstract

Undetected cracks in materials like concrete, asphalt, metals, and composites jeopardize structural integrity, posing safety and economic risks across infrastructure, aerospace, and automotive sectors. This study proposes a Crack-Aware CNN-ViT Hybrid model for real-time crack detection, integrating a Crack-Aware Attention Module (CAM) to emphasize crack geometry and a Crack Severity Annotation Framework to classify cracks by width, depth, and impact. Trained on a 60,000-image RGB dataset, augmented with conditional Generative Adversarial Networks for diverse materials and conditions, the model achieves 95.3% ± 0.2% accuracy, 94.2% ± 0.3% precision, 96.0% ± 0.2% recall, 95.1% ± 0.2% F1 score, and 90.5% ± 0.4% IoU at 32 fps, processing webcam feeds on an NVIDIA Jetson Orin Nano. Ablation studies, cross-dataset validation on SDNET2018 and CrackTree260, and a real-world bridge inspection demonstrate statistically significant improvements over YOLOv8 (by 5.1% accuracy) and Vision Transformers. Enabling automated, edge-based monitoring with timestamped crack storage, this scalable solution advances structural health monitoring, ensuring predictive maintenance and safety.