GPU Architecture for Unsupervised Surface Inspection Using Multi-scale Texture Analysis

Abstract

Surface inspection in manufacturing scenarios is strongly related to accuracy and runtime requirements. To ensure high accuracy and reliable defect detection results, in many applications the environment will be modified with respect to constant illumination and well defined system behavior. In these cases early vision algorithms like edge detection or thresholds are applied for surface inspection which are not complex and runtime intensive. In more complex scenarios with changing illumination conditions more elaborate image processing techniques are needed to ensure reliable defect detection, which leads to more runtime intensive algorithms. To overcome this challenges time-consuming operations can be transferred to additional hardware to satisfy the strong runtime constrains even for complex image processing techniques. The graphics processing unit (GPU) as a co-processor offers great potential and massively parallel computing power to enable real-time application of complex computing steps in production scenarios. We introduce a GPU based implementation of unsupervised defect detection on textured surfaces. Evaluation on an artificial dataset confirms excellent defect detection results and real-time performance.