裂缝检测对维护建筑物结构安全至关重要，近年来，基于深度学习的卷积神经网络为裂缝检测提供了新的解决方案。但这是以巨大的计算资源为代价的，因此在实际应用中存在实时性差、检测效率低的问题。针对此问题，提出了一种基于U-Net架构的轻量化多尺度动态融合卷积模块（multi-scale dynamic fusion convolution, MSFC），以提高裂缝分割的效率。为验证所提方法的有效性，构建了一个包含2 045张裂缝图像的数据集Crack2045，并在该数据集上进行了实验。实验结果表明：与原始U-Net模型相比，采用MSFC模块的模型在保持准确率不下降的情况下减少了78.51%的参数量和63.75%的计算量。同时，MSFC模块具有一定的泛化性，能够无缝集成到不同的语义分割模型中。研究结果不仅为裂缝检测提供了一种高效的深度学习方法，也为资源受限环境下的模型部署提供了新的可能性。

Crack detection is crucial to maintaining the structural safety of buildings. In recent years, convolutional neural networks based on deep learning have provided new solutions for crack detection. However, this comes at the cost of huge computing resources, so there are problems of poor real-time performance and low detection efficiency in practical applications. To address this problem, a lightweight MSFC (multi-scale dynamic fusion convolution module) based on the U-Net architecture was proposed to improve the efficiency of crack segmentation. To verify the effectiveness of the proposed method, a dataset Crack2045 containing 2 045 crack images was constructed and experiments were conducted on this dataset. The experimental results show that compared with the original U-Net model, the model using the MSFC module reduces 78.51% of the parameters and 63.75% of the computational complexity while maintaining the same accuracy. At the same time, the MSFC module has a certain degree of generalization and can be seamlessly integrated into different semantic segmentation models. This study not only provides an efficient deep learning method for crack detection, but also provides new possibilities for model deployment in resource-constrained environments.