Abstract: To ensure road quality and safety, automated crack detection is crucial for the maintenance of concrete pavements. To address the issue of pixel information loss caused by excessive down-sampling in existing deep learning-based methods, a concrete crack detection network based on progressive context interaction and attention mechanisms was proposed. First, an optimized UNet++ was used as the backbone, where asymmetric convolution blocks were applied to enhance feature extraction. Second, Progressive Context Interaction Mechanism (PCIM) was introduced to efficiently capture and fuse multi-scale features across adjacent stages. Third, in the feature enhancement phase, the Attention Combination (AC) approach was used to improve feature representation capability. Finally, in the feature fusion phase, a Multi-Semantic Attention Dynamic Fusion Module (MADFM) was utilized to enhance detail recovery and retention. The testing results on three public datasets (DeepCrack, Crack500, and CFD) show that, compared to DeepCrack, CrackFormer, and PAF-Net (Progressive and Adaptive Feature Fusion Network), the proposed method achieves superior performance. The F-score on the DeepCrack test set has been improved by 1.33, 5.07, and 3.93 percentage points, respectively; on the Crack500 test set, it has been enhanced by 3.04, 4.35, and 0.82 percentage points, respectively; and on the CFD test set, it has been increased by 3.03, 6.00, and 4.73 percentage points, respectively. These results fully prove that the proposed method has achieved enhanced accuracy in crack detection and exhibits excellent robust performance across different test sets.

Key words: UNet++, crack detection, asymmetric convolution, multi-scale feature fusion, attention mechanism

摘要： 为保障道路质量与安全，自动化裂缝检测在混凝土路面维护中至关重要。针对现有基于深度学习的检测方法因过度下采样导致裂缝像素信息丢失的问题，提出一种基于渐进式上下文交互和注意力机制的混凝土裂缝检测网络。首先，以优化的UNet++为主干，采用非对称卷积块来增强特征提取能力；其次，引入渐进式上下文交互机制（PCIM）以高效捕捉与融合相邻特征图的多尺度特征；再次，在特征增强阶段，用注意力组合（AC）方式提高特征表达能力；最后，在特征融合阶段，使用多语义注意力动态融合模块（MADFM），以增强细节恢复和保留效果。在3个公开数据集上的测试结果表明，相较于DeepCrack、CrackFormer、PAF-Net (Progressive and Adaptive Feature Fusion Network)，所提方法的性能更优。在DeepCrack测试集上，F-score分别提高了1.33、5.07和3.93个百分点;在Crack500测试集上，分别提升了3.04、4.35和0.82个百分点；在CFD测试集上，分别提升了3.03、6.00和4.73个百分点，这些结果充分验证了所提方法在裂缝检测准确性方面实现提升，同时在不同测试集上均展现出出色的鲁棒性能。

关键词: UNet++, 裂缝检测, 非对称卷积, 多尺度特征融合, 注意力机制