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Eddy current testing (ECT) is an effective technique in the evaluation of thedepth of metal surface defects. However, in practice, the evaluation primarilyrelies on the experience of an operator and is often carried out by manualinspection. In this paper, we address the challenges of automatic depthevaluation of metal surface defects by virtual of state-of-the-art deeplearning (DL) techniques. The main contributions are three-fold. Firstly, ahighly-integrated portable ECT device is developed, which takes advantage of anadvanced field programmable gate array (Zynq-7020 system on chip) and providesfast data acquisition and in-phase/quadrature demodulation. Secondly, adataset, termed as MDDECT, is constructed using the ECT device by humanoperators and made openly available. It contains 48,000 scans from 18 defectsof different depths and lift-offs. Thirdly, the depth evaluation problem isformulated as a time series classification problem, and variousstate-of-the-art 1-d residual convolutional neural networks are trained andevaluated on the MDDECT dataset. A 38-layer 1-d ResNeXt achieves an accuracy of93.58% in discriminating the surface defects in a stainless steel sheet. Thedepths of the defects vary from 0.3 mm to 2.0 mm in a resolution of 0.1 mm. Inaddition, results show that the trained ResNeXt1D-38 model is immune tolift-off signals.

Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks