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DETECTION AND CLASSIFICATION OF SYMBOLS IN PRINCIPLE SKETCHES USING DEEP LEARNING

Published online by Cambridge University Press:  27 July 2021

Sebastian Bickel ,
Benjamin Schleich  and
Sandro Wartzack
Sebastian Bickel*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Benjamin Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
*
Bickel, Sebastian, Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design, Germany, bickel@mfk.fau.de

Abstract

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Data-driven methods from the field of Artificial Intelligence or Machine Learning are increasingly applied in mechanical engineering. This refers to the development of digital engineering in recent years, which aims to bring these methods into practice in order to realize cost and time savings. However, a necessary step towards the implementation of such methods is the utilization of existing data. This problem is essential because the mere availability of data does not automatically imply data usability. Therefore, this paper presents a method to automatically recognize symbols from principle sketches, which allows the generation of training data for machine learning algorithms. In this approach, the symbols are created randomly and their illustration varies with each generation. . A deep learning network from the field of computer vision is used to test the generated data set and thus to recognize symbols on principle sketches. This type of drawing is especially interesting because the cost-saving potential is very high due to the application in the early phases of the product development process.

Keywords

Artificial intelligenceMachine learningEarly design phases
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1183 - 1192
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Deng, X., Li, T., Xu, Y. Cao, Y., Kong, C., Zhang, E., (2020), The Computer Vision-based Tolerancing Callout Detection Model. Procedia CIRP. 92. 134-139. https://doi.org/10.1016/j.procir.2020.05.189CrossRefGoogle Scholar
Elyan, E., Moreno-García, C., Jayne, C., (2018), Symbols Classification in Engineering Drawings. International joint conference on neural networks 2018 (IJCNN), 8-13 July 2018, Rio de Janeiro, Brazil https://dx.doi.org/10.1109/IJCNN.2018.8489087.Google Scholar
Everingham, M., Eslami, S.M.; Van Gool, L., Williams, C., Winn, J., Zissermann, A. (2015), The Pascal Visual Object Classes Challenge: A Retrospective. International Journal of Computer Vision Volume 111 (2015) 1, pp. 98–136. https://doi.org/10.1007/s11263-014-0733-5CrossRefGoogle Scholar
Fu, L., Kara, L., (2011). From engineering diagrams to engineering models: Visual recognition and applications. Computer-Aided Design. 43. 278-292. https://doi.org/10.1016/j.cad.2010.12.011CrossRefGoogle Scholar
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014), Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 580–587, https://dx.doi.org/10.1109/CVPR.2014.81CrossRefGoogle Scholar
Girshick, R. (2015), “Fast R-CNN,” IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1440–1448, https://dx.doi.org/10.1109/ICCV.2015.169CrossRefGoogle Scholar
Goetz, S., Schleich, B., Wartzack, S. (2018), A new approach to first tolerance evaluations in the conceptual design stage based on tolerance graphs, Procedia CIRP, Volume 75, Pages 167-172 https://doi.org/10.1016/j.procir.2018.04.030.Google Scholar
He, K.; Gkioxari, G.; Dollár, P.; Girshick, R., et al. (2017) Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 2980–2988, https://dx.doi.org/10.1109/ICCV.2017.322CrossRefGoogle Scholar
Kasturi, R., Bow, S. T., El-Masri, W., Shah, J., Gattiker, J. R. and Mokate, U. B. (1990), “A system for interpretation of line drawings,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, no. 10, pp. 978–992, https://dx.doi.org/10.1109/34.58870 .CrossRefGoogle Scholar
Lawrence, S., Giles, C. L., Tsoi, Ah Chung and Back, A. D.(1997), “Face recognition: a convolutional neural-network approach,” in IEEE Transactions on Neural Networks, Vol. 8, no. 1, pp. 98–113, https://dx.doi.org/10.1109/72.554195CrossRefGoogle Scholar
Leibe, B., Leonardis, A., Schiele, B. (2004), Combined object categorization and segmentation with an implicit shape model. Proceedings 8th Eur. Conference. Computer Vis. (ECCV).Google Scholar
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C. L. (2014) Microsoft COCO: Common Objects in Context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_48CrossRefGoogle Scholar
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017), Feature Pyramid Networks for Object Detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 936–944, https://dx.doi.org/10.1109/CVPR.2017.106CrossRefGoogle Scholar
Lindemann, U. (2008), Methodische Entwicklung technischer Produkte: Methoden flexibel und situationsgerecht an-wenden. Berlin u. a.: Springer-Verlag, 2008, https://dx.doi.org/10.1007/978-3-642-01423-9CrossRefGoogle Scholar
Okazaki, A., Kondo, T., Mori, K., Tsunekawa, S. and Kawamoto, E. (1988), An automatic circuit diagram reader with loop-structure-based symbol recognition, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, no. 3, pp. 331–341, https://dx.doi.org/10.1109/34.3898 .CrossRefGoogle Scholar
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017, https://dx.doi.org/10.1109/TPAMI.2016.2577031CrossRefGoogle Scholar
Roth, K. (2000), Konstruieren mit Konstruktionskatalogen: Band 1: Konstruktionslehre. 3., Aufl. 2000. Springer-Verlag Berlin Heidelberg https://doi.org/10.1007/978-3-642-17466-7CrossRefGoogle Scholar
Roth, K. (2001), Konstruieren mit Konstruktionskatalogen: Band 2: Kataloge. 3., Aufl. 2000. Springer-Verlag Berlin Heidelberg https://doi.org/10.1007/978-3-642-17467-4CrossRefGoogle Scholar
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., Fei-Fei, L. (2014), ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115, 211–252. https://doi.org/10.1007/s11263-015-0816-yCrossRefGoogle Scholar
Sinha, R. K., Pandey, R., Pattnaik, R. (2017), Deep Learning For Computer Vision Tasks: A Review, International Conference on Intelligent Computing and Control (I2C2) https://doi.org/10.1155/2018/7068349CrossRefGoogle Scholar
VDI 2222 (1997), Methodic development of solution principles. Düsseldorf, VDIGoogle Scholar
VDI 2221 (2019), Design of technical products and systems - Model of product design. Düsseldorf, VDIGoogle Scholar
VDI 2223 (2004), Systematic embodiment design of technical products. Düsseldorf, VDIGoogle Scholar
Wiley V., Lucas, T. (2018), Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research Volume 2, pp. 28–36. https://doi.org/10.29099/ijair.v2i1.42Google Scholar