DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation

In construction quality monitoring, accurately detecting and segmenting cracks in concrete structures is paramount for safety and maintenance. Current convolutional neural networks (CNNs) have demonstrated strong performance in crack segmentation tasks, yet they often struggle with complex backgrounds and fail to capture fine-grained tubular structures fully. In contrast, Transformers excel at capturing global context but lack precision in detailed feature extraction. We introduce DSCformer, a novel hybrid model that integrates an enhanced Dynamic Snake Convolution (DSConv) with a Transformer architecture for crack segmentation to address these challenges. Our key contributions include the enhanced DSConv through a pyramid kernel for adaptive offset computation and a simultaneous bi-directional learnable offset iteration, significantly improving the model's performance to capture intricate crack patterns. Additionally, we propose a Weighted Convolutional Attention Module (WCAM), which refines channel attention, allowing for more precise and adaptive feature attention. We evaluate DSCformer on the Crack3238 and FIND datasets, achieving IoUs of 59.22% and 87.24%, respectively. The experimental results suggest that our DSCformer outperforms state-of-the-art methods across different datasets.