Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards
This study proposes to automate the analysis of wiring diagrams to generate cable lists by using machine learning for text classification and pre-trained Deep Neural Network (DNN)-based image classification to detect cable routes. In shipyards, many drawings are produced for each ship, and analyzing...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | International Journal of Naval Architecture and Ocean Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2092678224000499 |
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| _version_ | 1846109760596738048 |
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| author | Adrian Rahmanto Putra Sol Ha Kwang-Phil Park |
| author_facet | Adrian Rahmanto Putra Sol Ha Kwang-Phil Park |
| author_sort | Adrian Rahmanto Putra |
| collection | DOAJ |
| description | This study proposes to automate the analysis of wiring diagrams to generate cable lists by using machine learning for text classification and pre-trained Deep Neural Network (DNN)-based image classification to detect cable routes. In shipyards, many drawings are produced for each ship, and analyzing these drawings, especially wiring diagrams, to generate cable lists for the Bill of Materials (BOM) can be a time-consuming and error-prone task. This process is performed manually by reading the cable routes and entering the information into a spreadsheet. To address these challenges, this study aims to automate the information extraction from wiring diagrams. The process involves extracting text from the PDF document and classifying it using machine learning, followed by using DNN-based image classification to trace cable routes and identify the relevant annotations. The developed algorithm was tested on three PDF wiring diagram samples and achieved an average accuracy of about 90%, confirming its effectiveness. |
| format | Article |
| id | doaj-art-0566e41c1e7b4126a6641f8e92ed67df |
| institution | Kabale University |
| issn | 2092-6782 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Naval Architecture and Ocean Engineering |
| spelling | doaj-art-0566e41c1e7b4126a6641f8e92ed67df2024-12-25T04:21:14ZengElsevierInternational Journal of Naval Architecture and Ocean Engineering2092-67822024-01-0116100630Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyardsAdrian Rahmanto Putra0Sol Ha1Kwang-Phil Park2Infoget System Co., Ltd., Seoul, Republic of KoreaSchool of Mechanical and Ocean Engineering, Mokpo National University, Jeollanam-do, Republic of Korea; Corresponding author.Department of Autonomous Vehicle System Engineering, Chungnam National University, Daejeon, Republic of Korea; Corresponding author.This study proposes to automate the analysis of wiring diagrams to generate cable lists by using machine learning for text classification and pre-trained Deep Neural Network (DNN)-based image classification to detect cable routes. In shipyards, many drawings are produced for each ship, and analyzing these drawings, especially wiring diagrams, to generate cable lists for the Bill of Materials (BOM) can be a time-consuming and error-prone task. This process is performed manually by reading the cable routes and entering the information into a spreadsheet. To address these challenges, this study aims to automate the information extraction from wiring diagrams. The process involves extracting text from the PDF document and classifying it using machine learning, followed by using DNN-based image classification to trace cable routes and identify the relevant annotations. The developed algorithm was tested on three PDF wiring diagram samples and achieved an average accuracy of about 90%, confirming its effectiveness.http://www.sciencedirect.com/science/article/pii/S2092678224000499Cable connection analysisWiring diagramMachine learningShip design automationText classificationLine detection |
| spellingShingle | Adrian Rahmanto Putra Sol Ha Kwang-Phil Park Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards International Journal of Naval Architecture and Ocean Engineering Cable connection analysis Wiring diagram Machine learning Ship design automation Text classification Line detection |
| title | Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards |
| title_full | Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards |
| title_fullStr | Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards |
| title_full_unstemmed | Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards |
| title_short | Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards |
| title_sort | automatic extraction of cable connection information from 2d drawings for electrical outfittings design in shipyards |
| topic | Cable connection analysis Wiring diagram Machine learning Ship design automation Text classification Line detection |
| url | http://www.sciencedirect.com/science/article/pii/S2092678224000499 |
| work_keys_str_mv | AT adrianrahmantoputra automaticextractionofcableconnectioninformationfrom2ddrawingsforelectricaloutfittingsdesigninshipyards AT solha automaticextractionofcableconnectioninformationfrom2ddrawingsforelectricaloutfittingsdesigninshipyards AT kwangphilpark automaticextractionofcableconnectioninformationfrom2ddrawingsforelectricaloutfittingsdesigninshipyards |