针对水下混凝土结构裂缝数据获取成本高、噪声干扰多且识别精度低的问题，提出一种基于图像矩理论和迁移学习的矩特征迁移（moment feature transfer network, MTNet）裂缝识别模型。为减小由数据不足引起的过拟合以及提高水下混凝土结构裂缝的识别精度，首先，提出多尺度矩特征算法提取裂缝的形状和纹理信息，降低背景噪声对裂缝识别的影响；其次，提出矩特征嵌入模块，该模块能有效地将多尺度矩特征算法融合到神经网络模型中；然后，提出MTNet模型用于裂缝识别，该模型融合了特征的矩信息和注意力模块，不仅可以提取裂缝特征的语义信息还能抑制背景噪声，从而提升水下混凝土结构裂缝分割质量；最后，建立水下混凝土裂缝数据集作为迁移学习的目标域数据集，以此降低样本不足对裂缝识别性能的影响。实验结果表明，本文所提出的裂缝识别MTNet模型具有较高的识别精度和泛化性能，能够准确识别复杂背景下水下混凝土的裂缝。

To address the challenges related to high costs associated with acquiring data of cracks on surface of underwater concrete structures, as well as the issues of significant noise interference and low identification accuracy, a novel crack identification model called moment feature transfer network (MTNet) based on image moment theory and transfer learning was proposed. To mitigate the limitations caused by insufficient data leading to overfitting and to enhance the identification accuracy of cracks of underwater concrete structures, several key contributions were made. Firstly, a multi-scale moment feature algorithm was introduced to extract both shape and texture information of cracks, thereby reducing the influence of background noise on crack identification. Secondly, a moment feature embedding module was developed to effectively integrate the multi-scale moment feature algorithm into the neural network model. The module facilitates the seamless incorporation of the algorithm into the model, ensuring enhanced identification performance. Furthermore, the MTNet model was presented for crack identification, combining the moment information of features and an attention module. This integration not only enabled the extraction of semantic information from crack features but also suppressed background noise, resulting in improved crack segmentation quality on underwater concrete structures. Lastly, a dedicated underwater concrete crack dataset was established as the target domain dataset for transfer learning, significantly alleviating the impact of inadequate samples on identification performance. Experimental results validate the efficacy of the proposed MTNet model, demonstrating its remarkable identification accuracy and generalization performance, showing that the model is able to effectively identify cracks on surface of underwater concrete structures, even in complex backgrounds.