Abstract
The radiographic inspection of weld beads is important to ensure quality and safety in pipe networks. Visual fatigue, distractions, and the amount of radiographic images to be analyzed can be listed as main factors for human inspection errors. This chapter presents an approach for automatically segmenting weld beads in Double Wall Double Image (DWDI) X-ray photographs by combining two known methods in the literature: Particle Swarm Optimization (PSO) and Dynamic Time Warping (DTW). Vertical profiles of the weld beads are obtained from the windows’ coordinates encoded by particles and compared, via DTW, with a predefined model. Experiments are performed considering two phases: first, tests are carried out to set the default configuration, and second the configured system (named WBdetect) is evaluated, including a comparison with another approach. Promising results show that WBdetect converges, most of the time, to the window that allows a proper segmentation of the weld bead, outperforming the compared approach (the average accuracy achieved by WBdetect is 93.63 \(+-\)12.91, and 65.88 \(+-\)17.9 % for the other approach).
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Acknowledgments
This work has been partially supported by the Brazilian National Research Council (Conselho Nacional de Pesquisa - CNPq), under research grant 309197/2014-7 to M.R. Delgado. The authors would also like to thank Leopoldo Americo Miguez de Mello R&D Center - CENPES, Brazilian Petroleum - PETROBRÁS.
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Miranda, R., Delgado, M., Mezzadri, T., Dutra da Silva, R., Vaz, M., Marinho, C. (2017). WBdetect: Particle Swarm Optimization for Segmenting Weld Beads in Radiographic Images. In: Nedjah, N., Lopes, H., Mourelle, L. (eds) Designing with Computational Intelligence. Studies in Computational Intelligence, vol 664. Springer, Cham. https://doi.org/10.1007/978-3-319-44735-3_12
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DOI: https://doi.org/10.1007/978-3-319-44735-3_12
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