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Multiple algorithm fusion in a corrosion detection algorithm used with shipboard tank and void inspection systems

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. Over this past year, we have extensively utilized the Insertable Stalk Inspection System (ISIS) to perform inspections of shipboard tanks and voids. This system collects between 15 and 30 images of the tank or void being inspected as well as a video archive of the complete inspection process. 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 consists of four independent algorithms that each separately assesses the coatings damage in each of the images that are analyzed. The algorithm results are fused to attain a single coatings damage value for each of the analyzed images. The damage values for each of the images are next aggregated in order to develop a single coatings damage value for the tank being inspected. This paper concentrates on the methods used to fuse the results from the four independent algorithms that assess corrosion damage at the individual image level as well as the methods used to aggregate the results from multiple images to attain a single coatings damage level. Results from both calibration tests and double blind testing are provided in the paper to demonstrate the advantages of the video inspection systems and the corrosion detection algorithm.