Improving edge detection in highly noised sheet-metal images

Abstract

This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system. The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.