Towards end-to-end pulsed eddy current classification and regression with CNN
This work addresses defect detection in multi-layer metal structures for non-destructive inspection, but it is incremental as it applies existing CNN techniques to a specific domain.
The paper tackles automated defect inspection in metal materials using pulsed eddy current data by proposing an end-to-end CNN model that simultaneously predicts defect class and depth, achieving higher accuracy and lower error compared to standard methods.
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.