  Eddy current testing (ECT) is one of the most popular Nondestructive Testing
(NDT) techniques, especially for conductive materials. Reconstructing the crack
profile from measured EC signals is one of the main goals of ECT. This task is
highly challenging, as the EC signals are nonlinear responses resulted from the
presence of cracks, and reconstructing the crack profile requires establishing
the forward model of the nonlinear electromagnetic dynamics and solving its
inverse problem, which is an ill-posed numerical optimization problem. Instead
of solving the inverse problem numerically, we propose to directly learn the
inverse mapping from EC signals to crack profiles with a deep encoder-decoder
convolutional neural network named EddyNet. EddyNet is trained on a set of
randomly generated crack profiles and the corresponding simulated EC responses
generated from a realistic forward model. On the held-out test data, EddyNet
achieved a mean absolute error of 0.198 between predicted profiles and ground
truth ones. Qualitatively, the geometries of predicted profiles are visually
similar to the ground truth profiles. Our method greatly reduces the usual
reliance on domain experts, and the reconstruction is extremely fast both on
GPUs and on CPUs. The source code of EddyNet is released on
https://github.com/askerlee/EddyNet.
