基于图像矩理论和迁移学习的水下 混凝土结构裂缝识别方法
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1.广州广检建设工程检测中心有限公司;2.广州大学风工程与工程振动研究中心;3.广州大学土木工程学院

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TP391

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国家重点研发计划项目(2022YFB2603300); );广州市基础研究计划市校(院)企联合资助项目(2024A03J0318); 高等学校学科创新引智计划(D21021); 广州市科技计划项目(20212200004)资助


Crack Identification Method for Underwater Concrete Structures Based on Image Moment Theory and Transfer Learning
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Guangzhou Guangjian Construction Engineering Testing Center Co., Ltd

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    摘要:

    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,本文提出一种基于图像矩理论和迁移学习的矩特征迁移(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。

    Abstract:

    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 is 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 are made. Firstly, a multi-scale moment feature algorithm is 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 is 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 is presented for crack identification, combining the moment information of features and an attention module. This integration not only enables the extraction of semantic information from crack features but also suppresses background noise, resulting in improved crack segmentation quality on underwater concrete structures. Lastly, a dedicated underwater concrete crack dataset is 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.

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罗创涟,周佛保,刘爱荣,等. 基于图像矩理论和迁移学习的水下 混凝土结构裂缝识别方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-09-25
  • 最后修改日期:2024-05-21
  • 录用日期:2024-05-29
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