A context-driven approach to image-based crack detection

• Published in: Machine vision and applications Vol. 27; no. 7; pp. 1103 - 1114
• Main Authors: Wang, Hongcheng, Xiong, Ziyou, Finn, Alan M., Chaudhry, Zaffir
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2016; Springer Nature B.V
• Subjects: Aircraft; Aircraft components; Communications Engineering; Computer Science; Context; Cracks; Decomposition; Flaw detection; Image contrast; Image detection; Image processing; Image Processing and Computer Vision; Inspection; Machine vision; Networks; Pattern Recognition; Principal components analysis; Robustness (mathematics); Short Paper; Vision systems; Context; Physical context; Robust principal component analysis (RPCA); Crack detection; Structural health monitoring; Geometric context
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-016-0779-1

We present a novel context-driven approach to image-based crack detection for automated inspection of aircraft surface and subsurface defects. In contrast to existing image-based crack detection methods, which rely mostly on low-level image processing and data-driven methods, our method explicitly incorporates multiple high-level context into low-level processing. We present two classes of context: geometric/structural context and physical context. We formulate mathematically a sparse decomposition problem to incorporate the context and apply robust principal component analysis to decompose typical repetitive rivet regions into a normal component and a sparse component. Cracks are detected in the sparse component. By applying the proposed context-driven approach to coated and uncoated test specimens, we achieve significant reduction in false detections compared to the approach without exploiting context.