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.
Welding is commonly used for connecting metal components in these critical metallic infrastructure, such as agricultural facilities, wind turbines, railways, bridges and pipelines. However, welding processes vulnerably lead to forming cracks, pores, and other defects on the surface. These defects not only could result in severer cracks and corrosion, but also may ultimately lead to malfunction and failure of metal components. Inspection of welds is thus critical to ensure the welding quality during fabrication, construction process, and later in-service stage. The visual inspection is the crucial and most cost-effective step to determine if the welding quality is passed or rejected. However, fast and accurately determining welding quality is a challenging task in the conventional visual inspection process, which is highly dependent on the experience and expertise of inspectors, and it is fairly subjective and sometimes even misleading. To meet the gap, we bring machine intelligence to welding visual inspection. Specifically, we developed a low-cost portable embedded device to support advanced machine learning algorithms for real-time welding image processing.