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12 April 2004 Fusion of color, grayscale, and edge-detection algorithms for the accurate assessment of corrosion in shipboard tank and void imagery
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Abstract
Over the last several years, the Naval Research Laboratory has developed video based systems for inspecting tanks (ballast, potable water, fuel, etc.) and other voids on ships. Using these systems, approximately 15 to 30 images of the coated surfaces of the tank or void being inspected are collected. A corrosion detection algorithm analyzes the collected imagery. The corrosion detection algorithm output is the percent coatings damage in the tank being inspected. The corrosion detection algorithm uses four independent algorithms that each separately assesses the coatings damage in each analyzed image. The independent algorithm results from each image are fused with other available information to develop a single coatings damage value for each of the analyzed images. The damage values for all of the images analyzed are next aggregated in order to develop a single coatings damage value for the complete tank or void being inspected. The results from this Corrosion Detection Algorithm have been extensively compared to the results of human performed inspections over the last two years.
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Bruce N. Nelson, Paul Slebodnick, William Groeninger, and Edward J. Lemieux "Fusion of color, grayscale, and edge-detection algorithms for the accurate assessment of corrosion in shipboard tank and void imagery", Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004); https://doi.org/10.1117/12.540920
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