Detection and inspection of metal surface corrosion in the ballast tanks of U.S. Navy ships has been a long time problem. The adverse climatic conditions to which the ballast tanks are exposed and the uneven geometry of ballast tanks makes the visual inspection process of surface coatings a difficult job. Thousands of tanks are inspected yearly, with the average cost of an individual tank inspection at approximately $8-15 thousand/each. To aid the visual inspection process, this research is conducted to develop a new technique to automate the visual task of metal surface inspection by image acquisition and post processing. The best results of image processing are achieved by the enhanced contrast between the paint defect and the background using a newly developed optically active additive (OAA) used in paints. Thorough investigation of image processing algorithms has been carried out and a background of imaging theory and experiments is illustrated in this work.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_theses-1335 |
| Date | 01 January 2004 |
| Creators | Kamat, Ashish V. |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of Kentucky Master's Theses |
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