Defect Detection of Steel Surfaces with Global Adaptive Percentile Thresholding of Gradient Image

Steel strips are used extensively for white goods, auto bodies and other purposes where surface defects are not acceptable. On-line surface inspection systems can effectively detect and classify defects and help in taking corrective actions. For detection of defects use of gradients is very popular in highlighting and subsequently segmenting areas of interest in a surface inspection system. Most of the time, segmentation by a fixed value threshold leads to unsatisfactory results. As defects can be both very small and large in size, segmentation of a gradient image based on percentile thresholding can lead to inadequate or excessive segmentation of defective regions. A global adaptive percentile thresholding of gradient image has been formulated for blister defect and water-deposit (a pseudo defect) in steel strips. The developed method adaptively changes the percentile value used for thresholding depending on the number of pixels above some specific values of gray level of the gradient image. The method is able to segment defective regions selectively preserving the characteristics of defects irrespective of the size of the defects. The developed method performs better than Otsu method of thresholding and an adaptive thresholding method based on local properties. © 2017, The Institution of Engineers (India).