Automatic extraction of cable connection information from 2D drawings for electrical outfittings design in shipyards
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (27)
- Adam, S., "Symbol and character recognition : application to engineering drawings", Int. J. Doc. Anal. Recogn, 3(2), pp.89~101 (2000)[DOI]
- Ahmed, Z., "Machine learning at Microsoft with ML.NET", Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019), ACM
- Arroyo, E., "Automatic derivation of qualitative plant simulation models from legacy piping and instrumentation diagrams", Comput. Chem. Eng, 92, pp.112~132 (2016)[DOI]
- Futatsumata, T., "Development of an automatic recognition system for plant diagrams", IAPR International Workshop on Machine Vision Applications (1990)
- Gimenez, L., "Review : reconstruction of 3D building information models from 2D scanned plans", J. Build. Eng, 2, pp.24~35 (2015)[DOI]
- Ha, J., "Method for pipe routing using the expert system and the heuristic pathfinding algorithm in shipbuilding", Int. J. Nav. Archit. Ocean Eng, 15, pp.100533~ (2023)[DOI]
- Han, Y. -S., "Assessment of ship-block stacking status based on images obtained from drone and geographic information system data", Int. J. Nav. Archit. Ocean Eng, 16, pp.100583~ (2024)[DOI]
- Karimpanal, T. G., "Self-organizing maps for storage and transfer of knowledge in reinforcement learning", Adapt. Behav, 27(2), pp.111~126 (2018)[DOI]
- Kim, M. -J., "Generating 3D texture models of vessel pipes using 2D texture transferred by object recognition", Journal of Computational Design and Engineering, 8(1), pp.475~487 (2021)[DOI]
- Kim, J. -H., "Hull form optimization of fully parameterized small ships using characteristic curves and deep neural networks", Int. J. Nav. Archit. Ocean Eng, 16, pp.100596~ (2024)[DOI]
- Kong, M. -C., "Variable indexing method in rule documents for ship design using extraction of portable document format elements", Journal of Computational Design and Engineering, 9(6), pp.2556~2573 (2022)[DOI]
- Kong, M. -C., "Object detection method for ship safety plans using deep learning", Ocean. Eng, 246, pp.110587~ (2022)[DOI]
- Krizhevsky, A., "ImageNet classification with deep convolutional neural networks", Commun. ACM, 60(6), pp.84~90 (2017)[DOI]
- Le, Q. V., "On optimization methods for deep learning", Proceedings of the 28th International Conference on Machine Learning, pp.265~272 (2011)
- Lee, W. -J., "Detection and tracking for the awareness of surroundings of a ship based on deep learning", Journal of Computational Design and Engineering, 8(5), pp.1407~1430 (2021)[DOI]
- Levenshtein, V. I., "Binary codes capable of correcting deletions, insertions and reversals", Sov. Phys. Dokl, 10(8), pp.707~710 (1966)
- Liu, D. C., "On the limited memory BFGS method for large scale optimization", Math. Program, 45, pp.503~528 (1989)[DOI]
- Liu, J., "Drawing deviation assessment of ship plate marking lines using progressive target extraction", Ocean. Eng, 270, pp.113650~ (2023)[DOI]
- Rahul, R., "Automatic information extraction from piping and instrumentation diagrams", Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM, pp.163~172 (2019), SciTePress
- Rho, J., "Automated BIM generation using drawing recognition and line-text extraction", J. Asian Architect. Build Eng, 20(6), pp.747~759 (2021)[DOI]
- Schraudolph, N.N, "A stochastic quasi-Newton method for online convex optimization", Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, pp.436~443 (2007)
- "pdfminer.six’s documentation" (2019)[URL]
- Son, E. -Y., "Corrosion area detection and depth prediction using machine learning", Int. J. Nav. Archit. Ocean Eng, 16, pp.100617~ (2024)[DOI]
- Troelsen, A., "Programming with WPF controls", Pro C# 5.0 and the.NET 4.5 Framework, pp.1157~1221 (2012), Apress
- Yu, E. -S., "Features recognition from piping and instrumentation diagrams in image format using a deep learning network", Energies, 12(23), pp.4425~ (2019)[DOI]
- Yun, D. -Y., "Deep neural network for automatic image recognition of engineering diagrams", Appl. Sci, 10(11), pp.4005~ (2020)[DOI]
- Zhao, Y., "Reconstructing BIM from 2D structural drawings for existing buildings", Autom. ConStruct, 128, pp.103750~ (2021)[DOI]