Segmentation of structural defects in polymer composite computed tomography images with deep learning models

We investigate appliance of different deep learning models to the problem of semantic segmentation of structural defects in computed tomography images of fiber-reinforced polymer composite material. Specifically, we try to segment porosities and delaminations in a specimen using U-Net and DeepLabv3 neural networks. We find out that complex models struggle to generalize solutions on small data samples that are generally available to individual research teams, whereas smaller models are the right choice for approaching defect segmentation in CT images. Our experiments are based on our own laboratory data, collected with X-ray microtomography and labeled manually for the semantic segmentation task.