Learning-based Automatic Defect Recognition with Computed Tomographic Imaging

The use of image-based automatic defect recognition (ADR) systems in a production line often requires strict processing-time specifications. On the other hand, the typical high-performance requirement of such system calls for the use of sophisticated, computationally-complex algorithms. Addressing the conflicting requirements of fast throughput and high detection performance is a significant challenge. In this paper we present a 3D learning-based ADR approach for industrial parts. The proposed method first extracts defect candidate regions using morphological closing and template matching. Then a local registration-based approach is utilized to produce accurate defect segmentation mask. Finally, 29 features including geometric features and texture features derived from grey level co-occurrence matrix are calculated for each candidate region, and a fast random forests classifier is used to classify the candidate regions as defect or defect-free. This approach was developed into a fully automated system for detecting casting defects in aluminum industrial parts depicted in 3D Computed Tomographic (CT) images. The system was tested on 31 images with 49 cavities and porosities defects, achieving a sensitivity of 94% with an average 3.5 false detections per part.