In this paper, we introduce a method to visually inspect industrial parts by using multi-view images of an industrial part on a rotating table. During the visual inspection of the parts' surfaces, the relationship between the camera pose, the light vectors, and the normal vectors of a part's surface is the key factor in detecting abnormalities. We can change the relationship between the three factors by rotating the part; then, the abnormalities are visible in certain positional relationships. We, therefore, track the points on a part's surfaces in an image sequence and discriminate abnormal points (such as points on scratches or dents) from normal points based on pixel value transitions while rotating the part. The experimental results, which were based on real data of industrial parts, showed that the proposed method could detect the abnormalities on the surfaces.
Tomoya Kaichi, Shohei Mori, Hideo Saito, Junichi Sugano, Hideyuki Adachi, "Multi-view Surface Inspection Using a Rotating Table" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision, 2018, pp 278-1 - 278-5, https://doi.org/10.2352/ISSN.2470-1173.2018.09.IRIACV-278