Pavement Crack Detection Method Based on Multi-Level Feature Fusion

Current U-Net-based pavement crack detection methods do not fully consider the interaction between the features of each level of the encoder, causing incomplete detection results or missed detections because of information loss during the downsampling process. To address this issue, this study proposes a pavement crack detection method based on multi-level feature fusion. In the encoding stage, the features of cracks at different levels are extracted to form crack feature representations from shallow to deep layers. In the skip connection section, a cross-level fusion strategy based on an improved Channel Cross Transformer (CCT) is adopted to enhance the complementarity between features at each level and enrich the expression of crack features. In the decoding stage, the feature fusion module is used to optimize the decoder's utilization of encoder features, promote the transmission of crack features, and improve the perception ability of crack features. In a series of comparative and ablation experiments on two public datasets, DeepCrack and CRACK500, the proposed method outperforms six other methods, including DeepCrack and Swin-UNet. On DeepCrack, the proposed method increases the F1 value by 2.30 and 2.51 percentage points, respectively, compared to those of DeepCrack and Swin-UNet, while on CRACK500, it increases by 1.65 and 1.00 percentage points, respectively.