An innovative approach is introduced to detect surface defects on titanium coated steel surfaces with varied size through the use of image processing techniques. This paper provides techniques which are useful to discover numerous kinds of surface defects present on coating surface. For defect detection, Firefly Algorithm (FA) based adaptive thresholding is proposed and is applied for the gray scale images. The FA ensuing nature inspired algorithm utilized expansively in support of determining various optimization problems and from the reconstructed image contours are extracted using level set method, the predictable images not including textures besides defects contours be compassed. The morphological post processing removes the noise in image and makes defects more distinguishable from the background. The speculative result persists in utilizing synchronous images of metal surface and shows that the proposed method can efficiently segment surface defects and obtain better performance than existing methods.

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.