  Eddy current testing (ECT) is an effective technique in the evaluation of the
depth of metal surface defects. However, in practice, the evaluation primarily
relies on the experience of an operator and is often carried out by manual
inspection. In this paper, we address the challenges of automatic depth
evaluation of metal surface defects by virtual of state-of-the-art deep
learning (DL) techniques. The main contributions are three-fold. Firstly, a
highly-integrated portable ECT device is developed, which takes advantage of an
advanced field programmable gate array (Zynq-7020 system on chip) and provides
fast data acquisition and in-phase/quadrature demodulation. Secondly, a
dataset, termed as MDDECT, is constructed using the ECT device by human
operators and made openly available. It contains 48,000 scans from 18 defects
of different depths and lift-offs. Thirdly, the depth evaluation problem is
formulated as a time series classification problem, and various
state-of-the-art 1-d residual convolutional neural networks are trained and
evaluated on the MDDECT dataset. A 38-layer 1-d ResNeXt achieves an accuracy of
93.58% in discriminating the surface defects in a stainless steel sheet. The
depths of the defects vary from 0.3 mm to 2.0 mm in a resolution of 0.1 mm. In
addition, results show that the trained ResNeXt1D-38 model is immune to
lift-off signals.
