Titanium (Ti) is an ideal structural material whose use is gradually emerging in civil engineering. Regular defect evaluation is indispensable during the long-term use of Ti sheets, which can be performed effectively using eddy current (EC) imaging, a method of visualizing defects that is convenient for inspectors. However, as EC scan images contain abundant information and have discrepancies in terms of their quality, it is difficult to extract effective features, thus affecting the evaluation results. In this article, we propose a method that combines the EC imaging technology with a quantitative evaluation method for Ti sheet defects based on the deep belief network (DBN) and least squares support vector machine (LSSVM). A multilayer DBN is constructed to extract the effective features from EC scan images for Ti sheet defects. Based on the extracted feature vectors, a multi-objective regression model of defect dimensions is established using the LSSVM algorithm. Then, the dimensions of Ti sheet defects such as length, diameter, and depth are quantitatively evaluated by the effective features and the efficient regression model. The experimental results show that the evaluation errors for the defect lengths and depths tested are less than 3 and 5%, respectively, confirming the validity of the proposed method.

钛（Ti）是一种理想的结构材料，其用途正逐渐在土木工程中出现。长期使用Ti板时，定期进行缺陷评估是必不可少的，这可以使用涡流（EC）成像有效地执行，涡流成像是一种便于检查人员使用的可视化缺陷的方法。然而，由于EC扫描图像包含大量信息并且在质量方面存在差异，因此难以提取有效特征，从而影响评估结果。在本文中，我们提出了一种基于深度置信网络（DBN）和最小二乘支持向量机（LSSVM）的，将EC成像技术与定量评估Ti薄板缺陷的方法相结合的方法。构建多层DBN可以从EC扫描图像中提取出Ti薄板缺陷的有效特征。基于提取的特征向量，使用LSSVM算法建立缺陷尺寸的多目标回归模型。然后，通过有效特征和有效回归模型定量评估Ti板缺陷的尺寸，例如长度，直径和深度。实验结果表明，缺陷长度和深度的评估误差分别小于3％和5％，证实了该方法的有效性。