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

Emanuel Aldea (1, 2) , Sylvie Le Hégarat-Mascle (1, 2)

1 SATIE-MOSS - Méthodes et Outils pour les Signaux et Systèmes
2 UP11 - Université Paris-Sud - Paris 11

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.

Mots clés: nondestructive examination; crack detection; image processing; visual inspection; a-contrario modeling

Domaines: Traitement du signal et de l'image [eess.SP]

Emanuel Aldea, Sylvie Le Hégarat-Mascle. Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework. Journal of Electronic Imaging, 2015, 24 (6), ⟨10.1117/1.JEI.24.6.061119⟩. ⟨hal-01691962⟩