Surface Defect Recognition of Hot-rolled Steel Plates Based on Tetrolet Transform

doi:10.3901/JME.2016.04.013

Abstract

Abstract: Sample images of hot-rolled steel plates are decomposed into multiple subbands with different scales and directions by Tetrolet transform. The high-pass Tetrolet coefficients of subbands are combined into a high-dimensional feature vector. Kernel locality preserving projection(KLPP) is applied to the high-dimensional feature vector for dimension reduction, which results in a low-dimensional feature vector. The low-dimensional feature vector is fed into support vector machine(SVM) for surface defect recognition of hot-rolled steel plates. The method is tested with sample images from an industrial production line, including transversal cracks, longitudinal cracks, transversal scratches, longitudinal scratches, scars, pimples, net cracks, impressions, scales and no defect. The results show that the recognition rate with Tetrolet transform is 97.38%, which is about 1% higher than that with Curvelet transform and Contourlet transform.

Key words: feature extraction, hot-rolled steel plates, multiscale geometric analysis, surface inspection, Tetrolet transform

CLC Number:

TP391

XU Ke, WANG Lei, WANG Jingyu. Surface Defect Recognition of Hot-rolled Steel Plates Based on Tetrolet Transform[J]. Journal of Mechanical Engineering, 2016, 52(4): 13-19.

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