Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

Liming Zhao; Yi Zhang; Xiaodong Xu; Hong Xiao; Chao Huang

doi:10.1117/12.2239254

27 September 2016 Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

Liming Zhao, Yi Zhang, Xiaodong Xu, Hong Xiao, Chao Huang

Proceedings Volume 9971, Applications of Digital Image Processing XXXIX; 99713B (2016) https://doi.org/10.1117/12.2239254
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

Citation Download Citation

Liming Zhao, Yi Zhang, Xiaodong Xu, Hong Xiao, and Chao Huang "Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99713B (27 September 2016); https://doi.org/10.1117/12.2239254

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