A Deep Belief network and Least Squares Support Vector Machine Method for Quantitative Evaluation of Defects in Titanium Sheet Using Eddy Current Scan Image

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.