Vision system for on-line surface inspection in aluminum casting process

In this paper an automated visual inspection system is described within a general architecture for the online detection and analysis of surface defects in the production of continuous flat metallic products. Real-time performance requirements result in the development of a high-parallel architecture for high-speed image processing. Matrix cameras are used for image acquisition instead of linear ones because of the surface appearance. Inspection is achieved with 1 mm² resolution. Similarity-based algorithms as well as texture algorithms have been developed and hardware-implemented for defect detection in a high-textured surface where, for the most of defects, segmentation can not be achieved by only means of threshold techniques. The system has been applied to continuous cast aluminum inspection, where up to fifteen different kinds of defects must be detected and classified. Online defect classification is attempted by means of the formal language theory