ASCBR-Net: A novel crack segmentation architecture with multi-scale atrous convolution, residual learning, and CBAM attention for generalizable concrete crack inspection in structural health monitoring systems

Existing crack segmentation methods perform well only on the specific datasets they were trained on, limiting their use in real structural health monitoring where cracks vary widely in scale and appearance. This paper proposes ASCBR-Net, a U-Net based architecture that embeds SE and spatial attention within each residual block and places a multi-rate ASPP at the bottleneck to capture multiscale context before spatial reconstruction. Evaluated on four merged public datasets under 5-fold cross-validation, ASCBR-Net outperformed seven baseline methods with statistically significant gains ( p < 0.01 ), improving F1-score by up to 9.30% and IoU by up to 7.61%.