首页|Researchers from University of Ulsan Describe Findings in Machine Learning (Corr osion area detection and depth prediction using machine learning)
Researchers from University of Ulsan Describe Findings in Machine Learning (Corr osion area detection and depth prediction using machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Ulsa n, South Korea, by NewsRx correspondents, research stated, "Corrosion reduces th e thickness of a structure, making it less safe and reducing its lifespan." Funders for this research include Ministry of Education-Singapore; National Re search Foundation of Korea; Korea Ministry of Small And Medium Enterprises And S tartups. Our news editors obtained a quote from the research from University of Ulsan: "I n particular, ships are vulnerable to corrosion because they are always submerge d in seawater. This corrosion is identified through regular inspections of the s hip structure, and gradually increases in scope if no action is taken at an earl y stage. In this study, we developed a model to detect the corrosion areas and p redict the depth of corrosion in the detected areas. The corrosion area detectio n model used a machine learning model based on Mask R-CNN. The 35,753 images wer e used to map corrosion images and measured corrosion depths. Four different col or maps and regression algorithm were used to predict corrosion depths and their performance was compared."
University of UlsanUlsanSouth KoreaAsiaCyborgsEmerging TechnologiesMachine Learning
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