摘要： 在现有基于U-Net的路面裂缝检测方法中，编码器各层次特征间的交互未能得到充分考虑，容易因下采样过程中的信息丢失而导致检测结果不完整或出现漏检。为此，本文提出一种基于多层次特征融合的路面裂缝检测方法。首先，在编码阶段，提取裂缝在不同层次上的特征，形成从浅层到深层的裂缝特征表示；其次，在跳跃连接，采用基于改进通道交叉Transformer的跨层次融合策略，增强各层次特征间的互补性，丰富裂缝特征的表达；最后，在解码阶段，利用特征融合模块，优化解码器对编码器特征的利用方式，促进裂缝特征的传递，提高对裂缝特征的感知能力。为验证所提方法的有效性，在DeepCrack和CRACK500两个公开数据集上进行一系列的对比实验和消融实验。实验结果表明，所提方法的综合表现优于DeepCrack、Swin-UNet等6种方法，在DeepCrack数据集上的F1分数分别提高了2.30%和2.51%，在CRACK500数据集上则分别提高了1.65%和1.00%。

Abstract: In existing pavement crack detection methods based on U-Net, the interaction between features at different levels of the encoder has not been fully considered, which may lead to incomplete crack detections or missed crack detections due to information loss during down-sampling. Therefore, this paper proposes a pavement crack detection method based on multi-level feature fusion. First, in the encoding stage, crack features at different levels are extracted from the input image, forming crack feature representations from shallow to deep levels. Second, in the skip connection, the cross-level fusion strategy based on improved channel-wise cross fusion Transformer is employed, which enhances the complementarity between features at different levels and enriches the representations of crack features. Finally, in the decoding stage, the feature cross fusion module is used to optimize how the decoder utilizes the encoder's features, promoting the transmission of crack features and improving the perception capability for crack features. To verify the effectiveness of the proposed method, a series of comparative experiments and ablation experiments were conducted on the two public datasets of DeepCrack and CRACK500. The experimental results show that the comprehensive performance of the proposed method is better than the six comparison methods including DeepCrack and Swin-UNet. Specifically, on the DeepCrack dataset, the F1 score increased by 2.30% and 2.51% respectively, while on the CRACK500 dataset, it increased by 1.65% and 1.00% respectively.