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http://dx.doi.org/10.21289/KSIC.2022.25.4.573

A Study on Image Preprocessing Methods for Automatic Detection of Ship Corrosion Based on Deep Learning  

Yun, Gwang-ho (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Oh, Sang-jin (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Shin, Sung-chul (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.25, no.4_2, 2022 , pp. 573-586 More about this Journal
Abstract
Corrosion can cause dangerous and expensive damage and failures of ship hulls and equipment. Therefore, it is necessary to maintain the vessel by periodic corrosion inspections. During visual inspection, many corrosion locations are inaccessible for many reasons, especially safety's point of view. Including subjective decisions of inspectors is one of the issues of visual inspection. Automation of visual inspection is tried by many pieces of research. In this study, we propose image preprocessing methods by image patch segmentation and thresholding. YOLOv5 was used as an object detection model after the image preprocessing. Finally, it was evaluated that corrosion detection performance using the proposed method was improved in terms of mean average precision.
Keywords
Corrosion; Image Preprocessing; Image Patch Segmentation; Thresholding; Deep Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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