  Pulsed eddy current (PEC) is an effective electromagnetic non-destructive
inspection (NDI) technique for metal materials, which has already been widely
adopted in detecting cracking and corrosion in some multi-layer structures.
Automatically inspecting the defects in these structures would be conducive to
further analysis and treatment of them. In this paper, we propose an effective
end-to-end model using convolutional neural networks (CNN) to learn effective
features from PEC data. Specifically, we construct a multi-task generic model,
based on 1D CNN, to predict both the class and depth of flaws simultaneously.
Extensive experiments demonstrate our model is capable of handling both
classification and regression tasks on PEC data. Our proposed model obtains
higher accuracy and lower error compared to other standard methods.
