Journal
JOURNAL OF MANUFACTURING SYSTEMS
Volume 57, Issue -, Pages 287-297Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2020.10.004
Keywords
Crack detection; Image processing; Computer vision; Percolation process; Metal crack
Categories
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2019R1I1A3A01059438]
- Chonnam National University [2018-3267]
- National Research Foundation of Korea [2019R1I1A3A01059438] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Crack detection is an important step in assessing the quality of pressed panel products. This paper presents a fast and non-invasive crack detection technique which involves extracting the outline of the captured object and applying a unique edge line evaluation. This technique is robust against environmental condition changes and only require a low-cost web camera. After capturing an image immediately following the press process, a clear one-pixel edge line is extracted by applying a light control and a series of pre-image processing algorithms, including a valley-emphasis Otsu method and percolation-based shape recognition. Next, the initial detection at low resolution is applied to search for every possible crack using unique edge line and curvature evaluation. Finally, at high resolution, the windowed image of every possible crack is individually analyzed to detect existing cracks using a more specific evaluation process. All of these steps are completed within 0.5 s, thus allowing for the technique to be applied in real-time on a highly automated manufacturing line. To demonstrate the performance of the proposed technique, experiments are conducted on an aluminum plate with different patterns and the pressed panel products. The results show that the proposed technique can detect surface cracks on pressed panels with stable performance as well as high accuracy and efficiency.
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