Visual inspection by capturing a rotating industrial part

Visual inspection is a vital step for maintaining the quality of industrial parts. In visual inspection, the relation between the three factors, camera pose, lights direction and normal vector of a part is very important to detect abnormalities on industrial part. We propose a visual inspection method for industrial parts using an image sequence captured while rotating an industrial part using a rotating table. By rotating a part, we can easily change the relation between three factors. We track the points on part's surfaces in an image sequence. We discriminate abnormal surface points such as scratches from the normal ones based on pixel value transitions in an image sequence. For accurately tracking the points on the part surface, we propose a novel camera calibration method of the rotating tabic and the camera with a telocentric lens and a pose estimation method for the rotating part. We use angular invariant feature vectors for improving robustness against types and shapes of industrial parts to be inspected. We presented experimental results using real data of industrial parts and verified that the proposed method could detect scratches on surfaces.