Development of a CNN edge detection model of noised X-ray images for enhanced performance of non-destructive testing

Zimu Xiao, Ki Young Song*, Madan M. Gupta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

X-ray non-destructive testing (NDT) is a primary detection technology in industrial fields, providing an effective detection for fragile and complex structures without destructing components. In this study, we adopt the principle of convolutional neural network (CNN) and a Laplacian filter to propose an edge detection model with improved performance. By constructing X-ray image datasets with different noise levels, our proposed CNN model successfully detects fuzzy defects on noised X-ray images, and presents better structure similarity of the detected information compared to conventional edge detection algorithms, Canny and SUSAN. Additionally, the experiment results indicate that the noised training datasets effectively improves the model's capability of noise resistance in edge detection tasks. Furthermore, the quality of training images significantly affects the performance of the trained model. This study develops a robust edge detection algorithm for low-cost and noise-independent X-ray non-destructive testing technology, providing a meaningful reference in edge detection of industrial X-ray images.

Original languageEnglish
Article number109012
JournalMeasurement: Journal of the International Measurement Confederation
Volume174
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Convolutional neural network
  • Edge detection
  • Noise resistance
  • Non-destructive testing
  • X-ray image

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