In-Su Han, Myung-Il Roh, Min-Chul Kong, Seong-Won Choi, Hwasup Jang, Yeonhwa Jo, Gapheon Lee, "A Method for the Automatic Revision Identification in Ship Drawings", Proceedings of ICCAS (International Conference on Computer Applications in Shipbuilding) 2026, Singapore, 2026.09.14-16
| Abstract | Surveyors of classification societies review shipyard drawings during the design stage to ensure safety and regulatory compliance. When revisions are required, the surveyors issue comments, and the designers modify and resubmit the drawings; this process is repeated until all requirements are satisfied. In practice, designers are expected to mark each revision with symbols such as revision clouds; however, these marks may be unintentionally omitted. Moreover, iterative revisions may lead to additional changes occurring beyond the marked areas, making the revision markings incomplete and less reliable. As a result, surveyors typically must rely on direct visual comparison of the original and revised drawings to verify the revisions, which is a significant challenge. Particularly when the size of drawings is vast, it not only consumes immense time and effort but also carries a high risk of overlooking revisions or making errors in judgment. In addition, repeated scanning or exporting during resubmissions can introduce non-revision inconsistencies that further obscure true changes. Therefore, this study proposed a method for automatically identifying revisions in ship drawings using deep learning-based change detection technology. To overcome the limitation of acquiring a large number of real revised drawings, the proposed method introduced a technique utilizing image inpainting and segmentation from the field of computer vision to generate synthetic revised drawings from original drawings. Specifically, we defined revisions as three representative types—addition, removal, and replacement—and generated synthetic revisions that closely resemble real revision patterns by reflecting these types. By generating numerous revised drawings with natural modifications applied to the original, we effectively constructed a synthetic training dataset. The change detection model, trained on the synthetic training dataset, rapidly and accurately identified revisions in actual drawings and presented them to the surveyors. Consequently, the surveyors could perform rapid, intensive reviews centered on the identified areas, significantly enhancing the efficiency of the iterative drawing review process. |
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| Publication Date | 2026-09-14 |
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