Defect detection of steel surface using entropy segmentation

Today, because of high manufacturing speed in steel industry, there is a need of fast and accurate detection of steel defect for quality assurance of product. Unlike other papers on defect detection of steel surface based on entropy this paper presents a new pre-processing and processing algorithm. The method presented here overcomes the limitations of traditional segmentation method or adaptive segmentation method like Otsu's method. This paper presents a new defect detection algorithm based on entropy. As a pre-processing step illumination compensation of image has been introduced using inverse illumination to remove non-uniformity of light intensity in the image. In the second part Local entropy of image has been used to detect the region of defect. The paper also suggests the concept of dynamic updation which helps to find a good background i.e. ideal steel surface and provides an effective method to classify the defects in its initial stage into defective and non-defective image. Background subtraction method is then used to extract the defective portion of image from the entropy image by comparing the entropy of image with the entropy of background image. Histogram thresholding method has been introduced to separate the background and defective portion in the background subtracted image to get the segmented image. The method was successfully tested on three kinds of defect on steel surface i.e. water droplet, blister and scratch.