Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.

No abstract

No abstract

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.Index Termssteel defect, entropy, image processing.

No abstract