Defect-Detection Technologies for Low-Observability Aircraft Skin Coatings - The Robotics Institute Carnegie Mellon University
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Defect-Detection Technologies for Low-Observability Aircraft Skin Coatings

Tech. Report, CMU-RI-TR-17-31, Robotics Institute, Carnegie Mellon University, May, 2017
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This report is a survey of the open literature on technologies for detection (finding) and characterization (area measurement and labeling of nature) of defects in low-observability (LO), or “stealth” coatings on aircraft. The focus is primarily on computer vision algorithms operating on images captured by conventional visible-light cameras, potentially with aids to detection such as specialized lighting schemes, stereoscopy, and other defect-depth characterization methods. A secondary focus is on alternative (and perhaps speculative) detection and characterization technologies, e.g., electromagnetic means outside the visible spectrum, ultrasonics, etc. Initial survey of open literature found no publications, patents, or supplier advertising except for several tablet-and-software based aids for alleviating the bookkeeping burden experienced by human visual inspectors. Our attention was therefore redirected toward the general field of computer-vision-based surface inspection, a field on which there is extensive literature addressing, e.g., in-process production of “web products” such as textiles, paper, and sheet metal.

However, the analogy of web-product manufacturing to defect detection and characterization of defects in LO coatings is weaker than one would like: in manufacturing, speed is more of an issue than comprehensive coverage, the focus is more on detection than on characterization, the total area that must be inspected has no upper bound, and each element of product area is typically seen only once. In contrast, there are only a few hundred LO airplanes in service, and each airplane is inspected frequently, at least after each flight and, after maintenance operations are completed, before the next flight.

This distinction in scenario suggests that a more fruitful path than one-shot detection and identification of defects might be a difference-based approach: how is the appearance of this surface patch different from its appearance upon the last inspection, and what is the meaning and importance of that difference? However, as an engineering problem vs. a computer-algorithm problem, success in difference-analysis will require precise spatial registration of images collected at different times, in turn requiring more precise knowledge of sensor package location in a coordinate system embedded in the aircraft surface than is required for one-shot web-product inspection.

We have collected, analyzed, and summarized in an annotated bibliography approximately 50 promising articles, patents, and manufacturer's brochures. This bibliography is meant to be read as part of the report.


author = {Mel Siegel and Cameron Riviere},
title = {Defect-Detection Technologies for Low-Observability Aircraft Skin Coatings},
year = {2017},
month = {May},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-17-31},

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