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Abstract Drawings such as piping and instrument diagrams (P&ID) or ship safety plans have various equipment and components (lines, signs, and text). Every drawing has rules to express these objects with specific symbols. Generally, these drawings are very complex because they are large in size and consist of relationships between several objects. Therefore, the drawing investigators spend countless time and labor.
For the above reasons, this study uses a deep learning model that has been actively researched recently. An object detection model based on deep learning can quickly find various objects within the drawing. However, the drawing differs from the common images in size and characteristics, generally used as an input in deep learning. Therefore, we proposed a series of procedures for applying the deep learning model to the drawing. This study proposed an object detection algorithm specialized for drawings by combining the non-maximum suppression (NMS) algorithm with the sliding window algorithm. YOLOv7 was selected as an object detection model, which showed the best accuracy by comparing various deep learning models. First, we made a detection window that slides on the drawing. Then, the NMS algorithm was applied to remove duplicate objects from the overall detection results.
Training a deep learning model requires a large amount of training data, but it takes a lot of time to label drawings manually. Therefore, we proposed a data generation model for training data. Objects and background images were extracted from several drawings, and training data were generated by randomly mixing them as material and base. The optimal parameters for training data were selected by comparing the accuracy of the drawings. All models used in this study were trained only with the generated virtual training data.
Knowing how many objects are placed in each division of the ship is important in the inspecting process. Therefore, we developed an algorithm that automatically recognizes the division of the ship and organizes the types and numbers of equipment placed in each division. Furthermore, we developed an algorithm that can obtain the connection relationship between objects and detailed specification of objects by recognizing lines and texts connected to each object.
The method proposed in this study was applied to several actual plans. We confirmed the effectiveness of the proposed method by obtaining high average accuracy. By applying the proposed method, the review procedure, which took several days, can be reduced dramatically to a few minutes per drawing.
Publication Date 2023-06-22
Min-Chul Kong, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "A Detection Method of Objects with Text in Drawings Based on Deep Learning", Proceedings of ISOPE(International Society of Offshore and Polar Engineers) 2023, Ottawa, Canada, 2023.06.19-23

List of Articles
번호 분류 제목 Publication Date
436 Domestic Conference 조두연, 차주환, 노명일, 최형순, 황호진, "3D 선체 모델과 Assembly Tree를 이용한 정반 배치 방법론 연구", 2011년도 대한조선학회 춘계학술발표회, 부산, pp. 524-527, 2011.06.02-03 file 2011-06-02
435 Domestic Conference 조두연, 송하철, 노명일, 김태영, 황호진, "3D 선체 모델을 이용한 블록 물류 관제 시뮬레이션 연구", 2010년도 대한조선학회 추계학술발표회, 창원, pp. 418-422, 2010.10.21-22 file 2010-10-21
434 Domestic Conference 정혁수, 이규열, 노명일, 김태완, "파이프 라우팅을 고려한 최적 장비 배치 알고리즘 개발", 2003년도 한국CAD/CAM학회 학술발표회, 서울, pp. 333-340, 2003.02.07 file 2003-02-07
433 Domestic Conference 정혁수, 이규열, 노명일, "파이프 라우팅 비용을 고려한 선박 기관실의 최적 장비 배치 설계에 관한 연구", 2003년도 대한조선학회 추계학술발표회, 경주, pp. 105-113, 2003.10.30-11.01 file 2003-10-30
432 Domestic Conference 정혁수, 이규열, 노명일, "다중 갑판을 고려한 함정의 최적 격실 배치 설계에 관한 연구", 2002년도 대한조선학회 춘계학술발표회, 부산, pp. 71-78, 2002.04.18-19 file 2002-04-18
431 Domestic Conference 정세용, 신현경, 구남국, 노명일, "최적화 기법을 이용한 부유식 해양 구조물의 배치 모델 연구", 2013년도 대한조선학회 춘계학술발표회, 제주, pp. 1049-1057, 2013.05.23-24 file 2013-05-23
430 Domestic Conference 정세용, 신현경, 구남국, 노명일, "최적화 기법을 기용한 부유식 해양 구조물의 다층 배치 모델 연구", 2013년도 대한조선학회 추계학술발표회, 울산, pp. 352-361, 2013.11.07-08 file 2013-11-07
429 Domestic Conference 정세용, 노명일, 신현경, "최적화 기법을 이용한 FPSO 상부 구조 모듈의 배치 방법 연구", 2013년도 한국CAD/CAM학회 학술발표회, 평창, pp. 826-830, 2013.01.30-02.01 file 2013-01-30
428 Domestic Conference 정선경, 노명일, 김기수, 김성균, 정동훈, "복합 성능을 고려한 함정의 배치 설계 방법에 관한 연구", 2016년도 한국CAD/CAM학회 동계학술발표회, 평창, pp. 158-161, 2016.01.27-29 file 2016-01-27
427 Domestic Conference 정선경, 노명일, 김기수, "복원성, 운용성 및 생존성을 고려한 함정의 최적 구획 배치 방법", 2016년도 대한조선학회 추계학술발표회, 창원, pp. 215, 2016.11.3-4 file 2016-11-03
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