A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Segmentation of thermal images for non-destructive evaluation of bridge decks
In this study, active IR thermography was applied to concrete bridge deck specimens in order to detect subsurface defects. A new region growing based algorithm was developed and implemented to automate the inspection by segmenting the images into defected and good regions. The maximum depth that can be detected using IR imaging was 3 in. (7.62 cm) and no surface cracks were detected by IR thermography. Also, due to the amount of noise in the image, pre-processing was necessary and proven to be effective. It is important to remember that the size of the masking window during the smoothing operation greatly affects the segmentation outcome. In this work, the masking window was limited to a size of 33. This algorithm is fully automated and no prior information is needed. The automation achieved in this project was in the analysis of IR images rather than in the acquisition phase. Once an IR image becomes available, the algorithm can analyze and determine if a defect exists without any human interference. Future work will focus on analyzing and optimizing the heating conditions for image acquisition using IR thermography in active and passive modes. This would include a detailed analysis of heating and cooling patterns of various types of defects to assist in identifying the signature of each type of defect. Also, future work will focus on the integration of the acquisition phase with the analysis algorithm to achieve a completely automated IR system that can signal a defect and identify its type. Moreover, while the algorithm accurately identifies defects and their locations accurately, it does not calculate the depths of defects. This will be one of the future research questions that will be investigated.
Segmentation of thermal images for non-destructive evaluation of bridge decks
In this study, active IR thermography was applied to concrete bridge deck specimens in order to detect subsurface defects. A new region growing based algorithm was developed and implemented to automate the inspection by segmenting the images into defected and good regions. The maximum depth that can be detected using IR imaging was 3 in. (7.62 cm) and no surface cracks were detected by IR thermography. Also, due to the amount of noise in the image, pre-processing was necessary and proven to be effective. It is important to remember that the size of the masking window during the smoothing operation greatly affects the segmentation outcome. In this work, the masking window was limited to a size of 33. This algorithm is fully automated and no prior information is needed. The automation achieved in this project was in the analysis of IR images rather than in the acquisition phase. Once an IR image becomes available, the algorithm can analyze and determine if a defect exists without any human interference. Future work will focus on analyzing and optimizing the heating conditions for image acquisition using IR thermography in active and passive modes. This would include a detailed analysis of heating and cooling patterns of various types of defects to assist in identifying the signature of each type of defect. Also, future work will focus on the integration of the acquisition phase with the analysis algorithm to achieve a completely automated IR system that can signal a defect and identify its type. Moreover, while the algorithm accurately identifies defects and their locations accurately, it does not calculate the depths of defects. This will be one of the future research questions that will be investigated.
Segmentation of thermal images for non-destructive evaluation of bridge decks
Segmentierung thermischer Bilder zur zerstörungsfreien Prüfung von Brückenbelägen
Abdel-Qader, Ikhlas (author) / Yohali, Solange (author) / Abudayyeh, Osama (author) / Yehia, Sherif (author)
NDT&E International ; 41 ; 395-405
2008
11 Seiten, 13 Bilder, 1 Tabelle, 22 Quellen
Article (Journal)
English
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