Imaging Components, Systems, and Processing

Vision-based surface defect inspection for thick steel plates

[+] Author Affiliations
Jong Pil Yun, Dongseob Kim

Korea Institute of Industrial Technology (KITECH), Daegyeong Division, Daegu, Republic of Korea

KyuHwan Kim, Sang Jun Lee

Pohang University of Science and Technology, Department of Electrical Engineering, Pohang, Republic of Korea

Chang Hyun Park

POSCO, Control and Instrumentation Research Group, Pohang, Republic of Korea

Sang Woo Kim

Pohang University of Science and Technology, Department of Electrical Engineering, Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang, Republic of Korea

Opt. Eng. 56(5), 053108 (May 22, 2017). doi:10.1117/1.OE.56.5.053108
History: Received November 10, 2016; Accepted April 26, 2017
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Abstract.  There are several types of steel products, such as wire rods, cold-rolled coils, hot-rolled coils, thick plates, and electrical sheets. Surface stains on cold-rolled coils are considered defects. However, surface stains on thick plates are not considered defects. A conventional optical structure is composed of a camera and lighting module. A defect inspection system that uses a dual lighting structure to distinguish uneven defects and color changes by surface noise is proposed. In addition, an image processing algorithm that can be used to detect defects is presented in this paper. The algorithm consists of a Gabor filter that detects the switching pattern and employs the binarization method to extract the shape of the defect. The optics module and detection algorithm optimized using a simulator were installed at a real plant, and the experimental results conducted on thick steel plate images obtained from the steel production line show the effectiveness of the proposed method.

© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Jong Pil Yun ; Dongseob Kim ; KyuHwan Kim ; Sang Jun Lee ; Chang Hyun Park, et al.
"Vision-based surface defect inspection for thick steel plates", Opt. Eng. 56(5), 053108 (May 22, 2017). ; http://dx.doi.org/10.1117/1.OE.56.5.053108


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