Development of Defect Detection Algorithm for Surface Inspection System Using Twin Illumination and Subtraction Technique

Surface inspection of steel products is very important for quality assurance. Many automatic in-line surface inspection systems using the camera technique have already been installed in sheet production lines. However, automatic surface inspection of steel products such as steel pipes and thick plates has not advanced because the entire product surface is covered with uneven mill scale, and it is difficult to distinguish the pattern of the mill scale from defects with concave-convex shapes in images of the camera.The authors developed a new surface inspection system using the twin illumination and subtraction technique, which emphasizes only concave-convex defects while canceling the pattern of the mill scale covering these types of steel products. An optical approach to enhance the detection performance of this system was already reported in connection with the development of a steel plate surface inspection technology. This paper discusses the development of a new image processing technique to detect defects from obtained images in order to satisfy the conditions of introducing automatic inspection systems at steel production lines. Concretely, a high-speed bright-dark pattern detection algorithm using expansion and conjunction processing was developed, and improved the processing speed by 22.6 times relative to the conventional simple method. An effective new original feature, overlap ratio of bright parts, was also developed, resulting in a 5.15% improvement in the classifier concordance rate relative to that without the proposed features. The developments described above have realized automatic surface inspection systems suitable for introduction in steel production manufacturing processes.