Browse > Article

Automatic Recognition of Symbol Objects in P&IDs using Artificial Intelligence  

Shin, Ho-Jin (School of Chemical Engineering and Materials Science, Chung-Ang University)
Jeon, Eun-Mi (School of Chemical Engineering and Materials Science, Chung-Ang University)
Kwon, Do-kyung (School of Computer Science and Engineering, Chung-Ang University)
Kwon, Jun-Seok (School of Computer Science and Engineering, Chung-Ang University)
Lee, Chul-Jin (School of Chemical Engineering and Materials Science, Chung-Ang University)
Publication Information
Plant Journal / v.17, no.3, 2021 , pp. 37-41 More about this Journal
Abstract
P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.
Keywords
Imaged Drawing Recognition; Piping and Instrumentation Diagram; Artificial Intelligence; Image Processing; Neural network image recognition model;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., 2016. "You only look once: Unified, real-time object detection." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788.
2 Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y. and Berg, A.C., 2016, October. "Ssd: Single shot multibox detector." In European conference on computer vision, pp. 21-37. Springer, Cham.
3 Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2012. "Imagenet classification with deep convolutional neural networks." In Advances in neural information processing systems, pp. 1097-1105.
4 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. "Going deeper with convolutions." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9.
5 Girshick, R., Donahue, J., Darrell, T. and Malik, J., 2014. "Rich feature hierarchies for accurate object detection and semantic segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587.
6 Redmon, J. and Farhadi, A., 2017. "YOLO9000: better, faster, stronger." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271.
7 Kang, S.O., Lee, E.B. and Baek, H.K., 2019. "A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)". Energies, 12(13), p.2593.   DOI
8 Yu, E.S., Cha, J.M., Lee, T., Kim, J. and Mun, D., 2019. "Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network". Energies, 12(23), p.4425.   DOI