Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems

Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework

[+] Author Affiliations
Emanuel Aldea, Sylvie Le Hégarat-Mascle

University Paris-Sud, Institute of Fundamental Electronics, Department of Autonomous Systems, Orsay cedex 91405, France

J. Electron. Imaging. 24(6), 061119 (Dec 30, 2015). doi:10.1117/1.JEI.24.6.061119
History: Received June 28, 2015; Accepted November 30, 2015
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Abstract.  We are interested in the performance of currently available algorithms for the detection of cracks in the specific context of aerial inspection, which is characterized by image quality degradation. We focus on two widely used families of algorithms based on minimal cost path analysis and on image percolation, and we highlight their limitations in this context. Furthermore, we propose an improved strategy based on a-contrario modeling which is able to withstand significant motion blur due to the absence of various thresholds which are usually required in order to cope with varying crack appearances and with varying levels of degradation. The experiments are performed on real image datasets to which we applied complex blur, and the results show that the proposed strategy is effective, while other methods which perform well on good quality data experience significant difficulties with degraded images.

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Citation

Emanuel Aldea and Sylvie Le Hégarat-Mascle
"Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework", J. Electron. Imaging. 24(6), 061119 (Dec 30, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.061119


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