Searching method for an optimal lighting application for defect detection by artificial vision in industrial field

Quality control in industrial application has greatly benefited from the development of tools like artificial vision. In order to obtain a good quality image of the object under investigation, the first step is to use a good lighting system. This paper presents a reliable method which allows to compare several lighting with respect to their capabilities of bringing out defects. This study has been led on textured industrial parts on which four types of defects have to be detected: smooth surfaces, bumps, lacks of material and hollows knocked surfaces. The aim is to determine the best lighting among various experimental sets. This method has two stages: the first one is a definition, according to the knowledge and the shape of the defects, of a pertinent attribute vector which components are defect sensitive. In the second step, discrimination power property of this vector is computed and compared under various illumination using Parzen's kernel. This method insures a well-suited illumination in numerous applications in defects detection and leads to an efficient set of lighting system and segmentation's parameters. Work is under way to generalize this method to multidimensional cases in order to allow interactions between components of the attribute vector.