CFM-Net with Multi-Scale Attention and Adaptive Fusion for Robust UAV-Based Bridge Crack Segmentation

To enhance crack detection accuracy during UAV-based inspections and address key challenges such as false positives from complex backgrounds, missed narrow cracks, and insufficient structural continuity modeling, this study proposes CFM-Net, a task-oriented segmentation network integrating Channel-Spatial Attention and Multi-Scale Structural Enhancement. Constructed on an optimized U-Net backbone, it employs three dedicated modules: the Channel and Spatial Attention Module (CBAM) to amplify crack-related features and suppress background interference; the Gated Fusion Module (GFF) to dynamically fuse multi-level features, improving detection of fine, narrow cracks; and the Morphology-Guided Multi-Scale Structural Perception Module (MGMSIB), designed to model the structural continuity and multi-scale characteristics of cracks. Comprehensive evaluations on the Mix Bridge Crack dataset demonstrate CFM-Net achieves competitive performance among the evaluated methods, with an mIoU of 80.05% and an F1-score of 87.06%. This represents a significant improvement over strong baselines, outperforming DeepCrack and CrackFormer by 2.3% and 2.42% in mIoU, and 1.21% and 1.03% in F1-score, respectively. Furthermore, the model demonstrates robust performance on heterogeneous crack datasets composed of multiple public sources, particularly in reducing false alarms, recovering narrow cracks, and maintaining crack topology. These results conclusively validate the effectiveness and practical utility of the proposed method for automated bridge crack inspection.