# Towards end-to-end pulsed eddy current classification and regression with CNN

## TL;DR

This paper introduces an end-to-end CNN-based model for automatic classification and depth regression of defects in pulsed eddy current data, improving accuracy over existing methods.

## Contribution

It presents a novel multi-task 1D CNN model that simultaneously predicts defect class and depth from PEC data, advancing automated NDI techniques.

## Key findings

- Higher accuracy in defect classification
- Lower error in depth regression
- Effective handling of both tasks simultaneously

## Abstract

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.