Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automated steel bridge coating rust defect recognition method based on color and texture feature
Abstract Most acquired steel bridge coating images are classified into two groups, defect and non-defect, by naked eyes or using the method reported in Lee's work [18]. Lee's method works effectively with blue-paint-coated steel bridge images in the State of Indiana, U.S.A. However, its effect on other coating colors or in some particular conditions, such as coating images with background noise or non-uniform illumination, has not yet been explored. In view of this, a rust defect recognition method based on color and texture feature (RUDERM), which combines the Fourier transform and color image processing, is proposed in this research. RUDERM aims to adapt to various background colors and overcome the influences from particular conditions. After comparisons of processed results, it is proven that RUDERM has an advantage in handling non-uniform illumination and can achieve a shorter processing time, which may lead to real-time coating inspection in the near future.
Highlights ► Most rust recognition methods cannot process rust images of red/brown paint colors. ► The developed RUDERM method can recognize rust on images of red/brown paint colors. ► RUDERM can effectively deal with non-uniform illumination and noise. ► RUDERM is efficient and has potential for real-time steel bridge coating assessment.
Automated steel bridge coating rust defect recognition method based on color and texture feature
Abstract Most acquired steel bridge coating images are classified into two groups, defect and non-defect, by naked eyes or using the method reported in Lee's work [18]. Lee's method works effectively with blue-paint-coated steel bridge images in the State of Indiana, U.S.A. However, its effect on other coating colors or in some particular conditions, such as coating images with background noise or non-uniform illumination, has not yet been explored. In view of this, a rust defect recognition method based on color and texture feature (RUDERM), which combines the Fourier transform and color image processing, is proposed in this research. RUDERM aims to adapt to various background colors and overcome the influences from particular conditions. After comparisons of processed results, it is proven that RUDERM has an advantage in handling non-uniform illumination and can achieve a shorter processing time, which may lead to real-time coating inspection in the near future.
Highlights ► Most rust recognition methods cannot process rust images of red/brown paint colors. ► The developed RUDERM method can recognize rust on images of red/brown paint colors. ► RUDERM can effectively deal with non-uniform illumination and noise. ► RUDERM is efficient and has potential for real-time steel bridge coating assessment.
Automated steel bridge coating rust defect recognition method based on color and texture feature
Shen, Heng-Kuang (Autor:in) / Chen, Po-Han (Autor:in) / Chang, Luh-Maan (Autor:in)
Automation in Construction ; 31 ; 338-356
06.11.2012
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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