Automatic localization of casting defects with convolutional neural networks

Automatic localization of defects in metal castings is a challenging task, owing to the rare occurrence and variation in appearance of defects. Convolutional neural networks (CNN) have recently shown outstanding performance in both image classification and localization tasks. We examine how several different CNN architectures can be used to localize casting defects in X-ray images. We take advantage of transfer learning to allow state-of-the-art CNN localization models to be trained on a relatively small dataset. In an alternative approach, we train a defect classification model on a series of defect images and then use a sliding classifier method to develop a simple localization model. We compare the localization accuracy and computational performance of each technique. We show promising results for defect localization on the GRIMA database of X-ray images (GDXray) dataset and establish a benchmark for future studies on this dataset.