Semantic segmentation for the analysis of creep voids in metallic materials

Semantic segmentation is the task of assigning a class or label to every pixel of the image. In addition to detecting objects, the semantic segmentation models also predict the shape, size, and location of each object in images. Deep learning-based segmentation has been used in challenging object detection tasks in several domains, such as autonomous vehicles, satellite images, and medical image diagnostics.

Within materials science, timely and reliable detection of creep voids in solid materials operating under high temperatures is vital for better life cycle management of valuable components. In this study, we present the application of a semantic image segmentation model for detecting creep voids in SEM images.

The semantic segmentation models generally consist of an encoder network followed by a decoder network. The encoder is usually a pre-trained classification network, such as VGG or ResNet. The decoder network projects the discriminative features learned by the encoder onto the pixel space, performing the classification task. To distinguish the creep voids from the normal surface of copper samples, we applied the DeepLab-v3+ model with encoders pre-trained on large datasets. Training the model with only 250 images for 200 epochs, we obtained a mean IoU score of 0.994 and a dice loss of 0.003. The generated segmentation maps provide information about the area fraction and number of creep voids.