Mr. Hoyeon Moon Profile

Hoyeon Moon

at Chonnam National Univ

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Proceedings Article | 3 April 2018 Presentation

Automated crack detection on pressed panel products using image processing (Conference Presentation)

Yeseul Kong, Hoyeon Moon, Hweekwon Jung, Gyuhae Park

Proceedings Volume 10602, 106020G (2018) https://doi.org/10.1117/12.2296490

KEYWORDS: Cameras, Image processing, Manufacturing, Inspection

Read Abstract +

Crack detection during the manufacturing process of pressed panel products is an important aspect of quality management. Tradition approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the press-forming process. In this study, we performed automated crack detection by integrating two image processing techniques with a multi-view-camera system. The first technique is based on evaluation of the edge lines which are extracted from a percolated object image. This technique could detect a crack without a reference image. Almost all of the edge lines of the panels show smooth variances of angle on the edges. When a crack occurs in panel products, an angle higher than 140 degree by the edge lines would appear, which could be used as an indication of crack presence. Another technique applies local image amplitude mapping (LAM) and compares a test image with the reference image. LAM is used to alleviate the problem associated with that the captured images during the manufacturing stage are not aligned against the reference image. The features created by LAM subtraction between the reference and test image are used to identify a crack. Before crack detection, multi-view images of a panel product are captured using multiple cameras. Afterwards, cracks are detected using both crack detection techniques based on image processing. The proposed technique is demonstrated in an actual manufacturing lines with real panel products. Experimental results clearly show that proposed technique could effectively improve the detection rate and speed for pressed panel products.

Proceedings Article | 10 May 2017 Presentation

Camera image processing for automated crack detection of pressed panel products (Conference Presentation)

Hwee Kwon Jung, Changwon Lee, Gyuhae Park, Hoyeon Moon

Proceedings Volume 10164, 1016409 (2017) https://doi.org/10.1117/12.2259812

KEYWORDS: 3D image reconstruction, Imaging systems, Cameras, Image processing, Manufacturing, Inspection, Image quality, Stereoscopic cameras, Associative arrays, 3D image processing

Read Abstract +

Crack detection on pressed panel during the press forming process is an important step to ensure the quality of panel products. Traditional crack detection technique has been generally performed by experienced human inspectors, which is subjective and expensive. Therefore, the implementation of automated and accurate crack detection is necessary during the press forming process. In this study, we performed an optimal camera positioning and automated crack detection using two image processing techniques with multi-view-camera system. The first technique is based on evaluation of the panel edge lines which are extracted from a percolated object image. This technique does not require a reference image for crack detection. Another technique is based on the comparison between a reference and a test image using the local image amplitude mapping. Before crack detection, multi-view images of a panel product are captured using multiple cameras and 3D shape information is reconstructed. Optimal camera positions are then determined based on the shape information. Afterwards, cracks are automatically detected using two crack detection techniques based on image processing. In order to demonstrate the capability of the proposed technique, experiments were performed in the laboratory and the actual manufacturing lines with the real panel products. Experimental results show that proposed techniques could effectively improve the crack detection rate with improved speed.

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