Advanced Hybrid Transformer–CNN Vision Framework for Automated Crack Detection to Enhance Structural Condition Assessment of Civil Structures

Reliable crack detection is essential for ensuring the safety, serviceability, and long-term performance of civil structures. Conventional manual inspections are labor-intensive and subjective, while existing computer vision models often exhibit reduced accuracy under variable field conditions. This study develops a computer vision-based automated crack detection framework utilizing a hybrid Transformer–CNN architecture to support infrastructure inspection and condition assessment. The proposed model leverages the global context modeling capability of Transformers and the local feature sensitivity of convolutional neural networks (CNNs) to enhance detection robustness. The optimized hybrid model achieved an Intersection over Union (IoU) of 91.8% and an accuracy of 98.7%, outperforming baseline CNN, Transformer-only, and LSTM architectures. Field validation on bridge inspection imagery demonstrated strong resilience to variations in illumination and texture. The developed approach contributes to digital inspection and intelligent lifecycle management of infrastructure assets by enabling reliable, automated, and non-intrusive structural condition evaluation under realistic field conditions.