Neural network segmentation methods for fatigue crack images obtained with X-ray tomography

We are proud to announce a new scientific paper derived from IDERPLANE project. Here below the paper information and open-access link.

Authors: XIAO Ce, BUFFIERE Jean-Yves

Partner involved: INSA LYON - MATEIS

Click here to reach Open Access Repository

Paper abstract:

Synchrotron X-ray tomography allows to observe fatigue crack propagation during in situ tests. Accurately segmenting the 3D shape of the cracks from the tomography image is essential for quantitative analysis. Fatigue cracks have small openings which result in low contrast images making crack segmentation difficult. Phase contrast available at synchrotron sources improves crack detection but it also increases the complexity of the image and human intervention is generally used to help traditional segmentation methods. In this work, an image segmentation method based on a convolutional neural network is developed to replace the user interpretation of images. Combined with a ’Hessian matrix’ filter, this method can successfully extract 3D shapes of internal fatigue cracks in metals.