关键词: 细粒度裂缝检测, 自注意力机制, 卷积神经网络（CNN）, 多尺度特征融合, 语义增强

Abstract: Fine-grained cracks, as an early stage of road surface crack formation, can be detected and repaired to eliminate safety hazards and reduce maintenance costs in a timely manner. Fine-grained cracks not only have complex topological structures, but also have geometric characteristics of small width and variable scale. In complex road backgrounds, existing methods are prone to missed detections and have low accuracy in perceiving their width. In response to this, this paper proposes a fine-grained crack detection method for road surfaces based on dual branch selective fusion and multi-scale semantic enhancement. An enhanced self-attention mechanism and a dual branch parallel backbone network of CNN (convolutional neural network) are designed to simultaneously extract features from both local and global perspectives, enriching feature representations layer by layer. A redundancy reduction and feature selective fusion (RSF) module is proposed to achieve the learning and interaction of dual branch global and local information, enhancing the expressive power of features. A multi-scale semantic enhancement fusion strategy is adopted to enhance the model’s perception ability of fine-grained crack features through cross scale information transmission and fusion. To validate the effectiveness and reliability of the proposed method, training and testing are conducted on the CrackTree260 public dataset, and the model’s generalization performance is evaluated on the CRKWH100 dataset. The experiment shows that the proposed method achieves ODS values of 0.909 and 0.918 on two datasets, respectively, which is superior to other advanced crack detection methods.

Key words: fine-grained crack detection, self-attention mechanism, convolutional neural network (CNN), multi-scale feature fusion, semantic enhancement