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.
Hoyeon Moon, Hwee Kwon Jung, Changwon Lee, and Gyuhae Park, "Camera image processing for automated crack detection of pressed panel products (Conference Presentation)," Proc. SPIE 10164, Active and Passive Smart Structures and Integrated Systems 2017, 1016409 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 26, 2017; Published: 10 May 2017); https://doi.org/10.1117/12.2259812.5427376564001.
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