【摘要】 针对传统超声导波成像检测方法难以精确表征结构损伤细节特征的问题，文中提出了基于深度学习的反卷积神经网络模型，对层合板中亚波长脱粘缺陷的超分辨成像问题进行研究，以获得损伤的细节特征。通过有限元仿真与全聚焦成像算法获取初始成像结果，再使用数据增强方法扩充数据库，最后对标注好的12 550张损伤图像进行训练和测试。研究结果表明，与原始全聚焦成像算法相比，反卷积神经网络模型下损伤的成像位置准确度提高了5%,成像精度高于91%,定位误差低于1.8 mm,说明文中所提方法能够明显提高网络成像结果分辨率并较好地显现亚波长损伤的细节特征。上述结果表明文中所提方法具有较高的检测效率，且无需人工经验，在工程实践中具有良好的应用价值。更多还原

【Abstract】 Traditional ultrasonic guided wave imaging detection methods are difficult to accurately characterize structural damage details. In order to obtain detailed features of the damage, the deconvolutional neural network model via deep learning is proposed to investigate the super-resolution imaging problem of subwavelength debonding defects in laminate plates. Initial imaging results are obtained by finite element simulation with the total focusing method. The labeled 12 550 damage images are trained and tested based on extended database expanded by data enhancement method. The results show that compared with the original full-focus imaging algorithm, the deconvolution neural network model improves the accuracy of damage location by 5%, the imaging accuracy is higher than 91%, and the positioning error is lower than 1.8 mm, indicating that the proposed method can significantly improve the resolution of network imaging results and better display the details of subwavelength damage. The above results show that the proposed method has high detection efficiency and does not require manual experience, and has good application value in engineering practice.更多还原