Vision-based defect detection of scale-covered steel billet surfaces
TLDR
The experimental results conducted on billet surface images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel billet surfaces.Abstract:
Vision-based inspection systems have been widely investigated for the detection and classification of defects in various industrial product. We present a new defect detection algorithm for scale-covered steel billet surfaces. Because of the availability of various kinds of steel, presence of scales, and manufacturing conditions, the features of billet surface images are not uniform. In particular, scales severely change the properties of defect-free surfaces. Moreover, the various kinds of possible defects make their detection difficult. In order to resolve these problems and to improve the detection performance, two methods are proposed. First, undecimated wavelet transform and vertical projection profile are presented. Second, a method for detecting the variations in the block features along the vertical direction is proposed. The former method can effectively detect vertical line defects, and the latter can efficiently detect the remaining defects, except the vertical line defects. The experimental results conducted on billet surface images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel billet surfaces.read more
Citations
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A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
Kechen Song,Yunhui Yan +1 more
TL;DR: Experimental results demonstrate that the proposed approach presents the performance of defect recognition under the influence of the feature variations of the intra-class changes, the illumination and grayscale changes, and even in the toughest situation with additive Gaussian noise, the AECLBP can still achieve the moderate recognition accuracy.
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Review of vision-based steel surface inspection systems
TL;DR: 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 using vision- based techniques.
Journal ArticleDOI
Defect detection for corner cracks in steel billets using a wavelet reconstruction method
TL;DR: A vision-based method for detecting corner cracks on the surface of steel billets based on a visual inspection algorithm is proposed to minimize the influence of scales and improve the accuracy of detection.
Proceedings ArticleDOI
Is overfeat useful for image-based surface defect classification tasks?
Pei-Hung Chen,Shen-Shyang Ho +1 more
TL;DR: A simple heuristic approach called Approximate Surface Roughness (ASR) is proposed that provides auxiliary information on the relationship between spatial regions in the defect image to be used together with the OverFeat features.
Journal ArticleDOI
Defect inspection system for steel wire rods produced by hot rolling process
Abstract: A vision-based inspection system has been investigated in order to improve the quality of products and processes found in various industries. In this paper, we propose a new defect detection algorithm for steel wire rods produced by the hot rolling process. Because the steel wire rods are long cylinder rods with a circular cross section, the brightness at the sides and center is inconsistent. Moreover, the various types of steel wire rods and the presence of scales affect the reflection properties of the rod surface. In order to resolve the abovementioned difficulties, the use of dynamic programming and a discrete wavelet transform are proposed. An adaptive local binarization method is used to further reduce the effects of scale. The effectiveness of the proposed method is shown by means of experiments conducted on images of steel wire rods that were obtained from an actual steel production line.
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