A Defect Recognition System for Automated Inspection of Non-rigid Surfaces

The goal of this work is the automated recognition of 3D surface defects for quality inspection in industrial production. For complexly shaped work pieces that are non-rigid and have non-uniform tolerance ranges, it is hard to distinguish acceptable surface deviations from defects. We propose a 3-stage defect recognition system based on 3D measurement of the defective part. First, a variable B-Spline surface model is used to adapt to acceptable tolerance ranges. The remaining model deviations are then used for segmentation of possible defects. Finally, a SVM-based classifier separates true defects from pseudo defects. On a real world data set of a series of measurements for a car front hood, the effectiveness of the approach is proven.