Strip wrinkling detection based on feature extraction and sparse representation Online publication date: Sun, 19-Mar-2017
by Wenhao Wang; Xiong Chen; Yuqi Pan
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 12, No. 1, 2017
Abstract: Strip wrinkling is a kind of adverse phenomenon occurring during strip steel production. It is necessary to detect strip wrinkling promptly for production lines, most of which rely on manual survey for detection until now. To provide a better condition for wrinkling detection, this paper introduces a feature-based image processing method. Feature extraction, dictionary learning and sparse representation are included in this method. Draped surface of wrinkling strip is suitable for SIFT technique extracting features of wrinkles. The extracted features will be collected by dictionary learning. For every original strip image, detecting wrinkles may be realised by assessing which dictionary its features incline to. At this stage, the assessment is implemented by sparse representation. The proposed method has been tested in a strip steel production line and the performance showed its applicability.
Online publication date: Sun, 19-Mar-2017
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