26 March 2001 Wavelet processing for image denoising and edge detection in automatic corrosion detection algorithms used in shipboard ballast tank video inspection systems
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Abstract
Over the past several years, the Naval Research Laboratory has been developing video inspection systems for assessing the coatings condition in shipboard ballast tanks. Two prototype systems have been configured and are presently being utilized to perform video inspections of dry and filled ballast tanks. These systems are described in this paper. The large size and low level lighting associated with this application results in 'noisy' imagery. A wavelet based de-noising method has been developed that removes the noise in the video imagery while maintaining other edges important to corrosion detection. Specific examples that demonstrate the efficacy of the de-noising methods are provided. Wavelet edge detection methods are then applied to the de-noised imagery to identify both regions of potential rust and the spatial distribution of rust. Additional methodologies are then utilized for final corrosion classification. The paper will provide examples of imagery collected in shipboard ballast tanks and examples of applying the automatic corrosion detection algorithms. These examples demonstrate the algorithms ability to work with 'noisy' imagery and to ignore objects in the imagery such as ladders and pipes. They also demonstrate the robustness of the developed automatic corrosion detection algorithms.
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Bruce N. Nelson, Bruce N. Nelson, Paul Slebodnick, Paul Slebodnick, Edward J. Lemieux, Edward J. Lemieux, William Singleton, William Singleton, Matt Krupa, Matt Krupa, Keith Lucas, Keith Lucas, E. Dail Thomas, E. Dail Thomas, Andrew Seelinger, Andrew Seelinger, } "Wavelet processing for image denoising and edge detection in automatic corrosion detection algorithms used in shipboard ballast tank video inspection systems", Proc. SPIE 4391, Wavelet Applications VIII, (26 March 2001); doi: 10.1117/12.421194; https://doi.org/10.1117/12.421194
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