Top > JRM > Lightweight Encoder with Attention Mechanism for Pipe Recognit ...

single-rb.php

JRM Vol.36 No.2 pp. 343-352
doi: 10.20965/jrm.2024.p0343
(2024)

Paper:

Views over last 60 days: 46

Lightweight Encoder with Attention Mechanism for Pipe Recognition Network

Yang Tian ORCID Icon, Xinyu Li, and Shugen Ma

Department of Robotics, Ritsumeikan University
1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan

Received:
September 27, 2023
Accepted:
January 12, 2024
Published:
April 20, 2024
Creative Commons License
Keywords:
object recognition, deep learning, BIM, pipe
Abstract

Utilizing building information modeling (BIM) for the analysis of existing pipelines necessitates the development of a swift and precise recognition method. Deep learning-based object recognition through imagery has emerged as a potent solution for tackling various recognition tasks. However, the direct application of these models is unfeasible due to their substantial computational requirements. In this research, we introduce a lightweight encoder explicitly for pipe recognition. By optimizing the network architecture using attention mechanisms, it ensures high-precision recognition while maintaining computational efficiency. The experimental results showcased in this study underscore the efficacy of the proposed lightweight encoder and its associated networks.

Recognition system in piping environment

Recognition system in piping environment

Cite this article as:
Y. Tian, X. Li, and S. Ma, “Lightweight Encoder with Attention Mechanism for Pipe Recognition Network,” J. Robot. Mechatron., Vol.36 No.2, pp. 343-352, 2024.
Data files:
References
  1. [1] R. Sacks, C. Eastman, G. Lee, and P. Teicholz, “BIM handbook: A guide to building information modeling for owners, designers, engineers, contractors, and facility managers,” John Wiley & Sons, 2018. https://doi.org/10.1002/9781119287568
  2. [2] S. Alizadehsalehi, A. Hadavi, and J. C. Huang, “From bim to extended reality in aec industry,” Automation in Construction, Vol.116, Article No.103254, 2020. https://doi.org/10.1016/j.autcon.2020.103254
  3. [3] A. Watson, “Digital buildings–challenges and opportunities,” Advanced Engineering Informatics, Vol.25, No.4, pp. 573-581, 2011. https://doi.org/10.1016/j.aei.2011.07.003
  4. [4] D. W. Green and M. Z. Southard, “Perry’s chemical engineers’ handbook,” McGraw-Hill Education, 2019.
  5. [5] D. Huber, B. Akinci, A. A. Oliver, E. Anil, B. E. Okorn, and X. Xiong, “Methods for automatically modeling and representing as-built building information models,” Proc. of the NSF CMMI Research Innovation Conf., Article No.856558, 2011.
  6. [6] Y.-J. Liu, J.-B. Zhang, J.-C. Hou, J.-C. Ren, and W.-Q. Tang, “Cylinder detection in large-scale point cloud of pipeline plant,” IEEE Trans. on Visualization and Computer Graphics, Vol.19, No.10, pp. 1700-1707, 2013. https://doi.org/10.1109/TVCG.2013.74
  7. [7] A. K. Patil, P. Holi, S. K. Lee, and Y. H. Chai, “An adaptive approach for the reconstruction and modeling of as-built 3d pipelines from point clouds,” Automation in Construction, Vol.75, pp. 65-78, 2017. https://doi.org/10.1016/j.autcon.2016.12.002
  8. [8] Y. Lin, Y. Tian, and X. Li, “Development of a pipe recognition network based on attention mechanism,” Proc. of JSME Annual Conf. on Robotics and Mechatronics (Robomech) 2022, 2P1-R08, 2022. https://doi.org/10.1299/jsmermd.2022.2P1-R08
  9. [9] F. Remondino and S. El-Hakim, “Image-based 3d modelling: A review,” The Photogrammetric Record, Vol.21, No.115, pp. 269-291, 2006. https://doi.org/10.1111/j.1477-9730.2006.00383.x
  10. [10] J. D. Markley, J. R. Stutzman, and E. N. Harris, “Hybridization of photogrammetry and laser scanning technology for as-built 3d cad models,” Proc. of 2008 IEEE Aerospace Conf., 2008. https://doi.org/10.1109/AERO.2008.4526650
  11. [11] R. B. Rusu, “Semantic 3d object maps for everyday manipulation in human living environments,” KI – Künstliche Intelligenz, Vol.24, No.4, pp. 345-348, 2010. https://doi.org/10.1007/s13218-010-0059-6
  12. [12] D. D. Patil and S. G. Deore, “Medical image segmentation: A review,” Int. J. of Computer Science and Mobile Computing, Vol.2, No.1, pp. 22-27, 2013.
  13. [13] Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.43, No.12, pp. 4338-4364, 2020. https://doi.org/10.1109/TPAMI.2020.3005434
  14. [14] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. of 2017 Int. Conf. on Engineering and Technology (ICET), 2017. https://doi.org/10.1109/ICEngTechnol.2017.8308186
  15. [15] R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 652-660, 2017. https://doi.org/10.1109/CVPR.2017.16
  16. [16] C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in Neural Information Processing Systems, Vol.30, 2017.
  17. [17] R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, “Imagenet-trained cnns are biased towards texture: Increasing shape bias improves accuracy and robustness,” arXiv preprint, arXiv:1811.12231, 2018. https://doi.org/10.48550/arXiv.1811.12231
  18. [18] Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1912-1920, 2015. https://doi.org/10.1109/CVPR.2015.7298801
  19. [19] W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert, “Pcn: Point completion network,” Proc. of 2018 Int. Conf. on 3D Vision (3DV), pp. 728-737, 2018. https://doi.org/10.1109/3DV.2018.00088
  20. [20] M. Aharchi and M. A. Kbir, “A review on 3d reconstruction techniques from 2d images,” Innovations in Smart Cities Applications Edition 3: Proc. of the 4th Int. Conf. on Smart City Applications, Vol.4, pp. 510-522, 2020. https://doi.org/10.1007/978-3-030-37629-1_37
  21. [21] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint, arXiv:1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556
  22. [22] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90
  23. [23] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2015. https://doi.org/10.1109/CVPR.2015.7298594
  24. [24] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint, arXiv:1704.04861, 2017. https://doi.org/10.48550/arXiv.1704.04861
  25. [25] X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 6848-6856, 2018. https://doi.org/10.1109/CVPR.2018.00716
  26. [26] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” Proc. of Int. Conf. on Machine Learning, pp. 6105-6114, 2019.
  27. [27] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.40, No.4, pp. 834-848, 2017. https://doi.org/10.1109/TPAMI.2017.2699184
  28. [28] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” Proc. of the European Conf. on Computer Vision (ECCV), pp. 801-818, 2018. https://doi.org/10.1007/978-3-030-01234-2_49
  29. [29] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Proc. of 18th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), Munich, Germany, October 5-9, 2015, Part III, pp. 234-241, 2015. https://doi.org/10.1007/978-3-319-24574-4_28
  30. [30] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90
  31. [31] X. Ding, X. Zhang, J. Han, and G. Ding, “Scaling up your kernels to 31x31: Revisiting large kernel design in cnns,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 11963-11975, 2022. https://doi.org/10.1109/CVPR52688.2022.01166
  32. [32] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018. https://doi.org/10.1109/CVPR.2018.00474
  33. [33] J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 7132-7141, 2018. https://doi.org/10.1109/CVPR.2018.00745
  34. [34] P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for activation functions,” arXiv preprint, arXiv:1710.05941, 2017. https://doi.org/10.48550/arXiv.1710.05941
  35. [35] Y. Tian, C. Ding, Y. F. Lin, S. Ma, and L. Li, “Automatic feature type selection in digital photogrammetry of piping,” Computer-Aided Civil and Infrastructure Engineering, Vol.37, No.10, pp. 1335-1348, 2022. https://doi.org/10.1111/mice.12840
  36. [36] Y. Liu, L. Chu, G. Chen, Z. Wu, Z. Chen, B. Lai, and Y. Hao, “Paddleseg: A high-efficient development toolkit for image segmentation,” arXiv preprint, arXiv:2101.06175, 2021. https://doi.org/10.48550/arXiv.2101.06175
  37. [37] A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan et al., “Searching for mobilenetv3,” Proc. of the IEEE/CVF Int. Conf. on Computer Vision, pp. 1314-1324, 2019. https://doi.org/10.1109/ICCV.2019.00140
  38. [38] Y. Tang, K. Han, J. Guo, C. Xu, C. Xu, and Y. Wang, “Ghostnetv2: Enhance cheap operation with long-range attention,” Advances in Neural Information Processing Systems, Vol.35, pp. 9969-9982, 2022.
  39. [39] M. Fan, S. Lai, J. Huang, X. Wei, Z. Chai, J. Luo, and X. Wei, “Rethinking bisenet for real-time semantic segmentation,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 9716-9725, 2021. https://doi.org/10.1109/CVPR46437.2021.00959
  40. [40] S. Tang, T. Sun, J. Peng, G. Chen, Y. Hao, M. Lin, Z. Xiao, J. You, and Y. Liu, “Pp-mobileseg: Explore the fast and accurate semantic segmentation model on mobile devices,” arXiv preprint, arXiv:2304.05152, 2023. https://doi.org/10.48550/arXiv.2304.05152
  41. [41] J. Peng, Y. Liu, S. Tang, Y. Hao, L. Chu, G. Chen, Z. Wu, Z. Chen, Z. Yu, Y. Du, Q. Dang, B. Lai, Q. Liu, X. Hu, D. Yu, and Y. Ma, “Pp-liteseg: A superior real-time semantic segmentation model,” arXiv preprint, arXiv:2204.02681, 2022. https://doi.org/10.48550/arXiv.2204.02681
  42. [42] C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, and N. Sang, “Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation,” Int. J. of Computer Vision, Vol.129, pp. 3051-3068, 2021. https://doi.org/10.1007/s11263-021-01515-2
  43. [43] E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems, Vol.34, pp. 12077-12090, 2021.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 01, 2024