Tunnel Crack Segmentation Algorithm Based on Feature Enhancement

Tunnel cracks, as one of the early indicators of tunnel damage, have a significant impact on the safe operation of tunnels. However, due to the complex lighting conditions, background noise, and diverse nature of cracks inside tunnels, traditional image segmentation algorithms often struggle to achieve sufficient accuracy in crack segmentation tasks. To address these challenges, this paper proposes a tunnel crack detection method. Firstly, a novel Dynamic Feature Enhancement Network (DFEN) module is designed to extract preliminary features using a feature extractor and selectively enhance feature representations through a gating mechanism. Additionally, a new Multi-Head Interaction Learning (MHIL) is introduced, facilitating feature sharing by incorporating information exchange across different attention heads, thereby improving feature representation capabilities. Lastly, the Adaptive Switchable Atrous Convolution (ASAC) module is introduced, combining the advantages of adaptive convolution and deformable convolution while incorporating Switchable Atrous Convolution (SAC) to enhance multi-scale feature capturing capabilities. Ablation experiments, through both qualitative and quantitative evaluations, demonstrate the effectiveness of the proposed method. Comparative analysis with existing semantic segmentation methods further confirms the superiority of the proposed method in tunnel crack detection.