Unsupervised deep learning for defect detection on CT parts using simulated data

Abstract

Computed tomography (CT) is a prominent technology for nondestructive quality control and is already used in industry for defect detection. However, as quality control is shifting towards a full in-line inspection, automatic CT analysis is required to meet the tight production time. Nonetheless, in settings where a high amount of data is produced, a robust fully automatic defect detection is essential In the past years, deep learning (DL) has been extensively used to perform vision tasks in an automatic way, and given its promising results, has been successfully applied in CT settings. Most of the recent work is based on supervised DL often adapted from results in the medical field. Supervised DL, although extremely powerful, has the drawbacks of requiring a high amount of labeled data done by experts and is biased to the specific dataset used. Therefore, an unsupervised DL model is presented. A two stages network formed by an auto-encoder and an autoregressive model, originally implemented for image generation, is adapted for volume segmentation. The network is trained on the specific task of defect segmentation of cast aluminum parts. CAD models of such parts are gathered, and corresponding simulated CT scans are acquired. Results show that the architecture, although originally implemented for data generation, can be adapted for CT volume segmentation.