Detection of surface coating defects using fuzzy C-means clustering with firefly optimization

Abstract

Defect detection in metallic surface images is a challenging task in the image analysis process. The data clustering and optimization techniques have been widely used for image segmentation and the combination of these two approaches improves the output stability as well as convergence speed. In this work developed an automatic, efficient method for the detection and segmentation of coating defects in metal surfaces. The Fuzzy c-means (FCM) and Firefly algorithm (FA) are well-known and popular methods to discover the image information comprising indiscriminate objects and solves many complex problems involved in image segmentation. In this paper, proposed a new technique for the coated metal surface defect detection using the hybridization of two methods, FCM with FA (FCM-FA). The results from experiments verified the efficiency of the developed FCM with FA over comparison with three existing methods in terms of evaluation parameters of defect detection for scanned high resolution images. It can be seen from the experimental results that the incorporated algorithm has the potential to segment and identify the defected regions from the coated surface.