An innovative application of pantograph recognition system based on deep learning

Research Article
Open access

An innovative application of pantograph recognition system based on deep learning

Handong Li 1* , Roberto Palacin 2 , Satnam Dlay 3
  • 1 Mathematics, Physics and Electrical Engineering, Northumbria University Newcastle, Newcastle upon Tyne, UK, NE1 8ST    
  • 2 Electrical and Electronic Engineering, School of Engineering, Newcastle University, Newcastle upon Tyne, UK, NE1 7RU    
  • 3 Electrical and Electronic Engineering, School of Engineering, Newcastle University, Newcastle upon Tyne, UK, NE1 7RU    
  • *corresponding author handong.li@northumbria.ac.uk
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230969
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

One of the most significant aspects for the correct operation of a modern railway electrification system is the health of the pantograph and overhead line. The application of machine vision technology to monitor the status of pantographs in real time can reduce pantographs and catenary accidents caused by unpredictable events. It is challenging to achieve real-time and accurate criteria with present pantograph detecting technologies. Therefore, this methodology collects pantograph images through high-definition cameras and transmits them to the cloud through 5G, use the Mask R-CNN algorithm to process and analyse the images., This technology can assist railway technicians in judging the status of the pantograph. Mask R-CNN employs the Resnet network for feature extraction. Resnet has the characteristics of cross-layer connection, which avoids the problem of network degradation due to the deep learning network being too deep, and greatly improves the training efficiency. The recognition matching degree of pantographs are greater than 0.975, enabling pantograph recognition in a variety of environmental conditions. The use of 5G connection increases transmission speed and allows for real-time detection of pantograph status, which is critical for the railway's automated operation.

Keywords:

5G, Mask-RCNN, Pantograph detection, Machine learning, Transport, Railway

Li,H.;Palacin,R.;Dlay,S. (2023). An innovative application of pantograph recognition system based on deep learning. Applied and Computational Engineering,6,827-833.
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References

[1]. R. Girshick, “Fast r-cnn”, Proceedings of the IEEE International Conference on Computer Vision, pp.1440 – 1448, 2015.

[2]. Q. Zhang, X. Chang and S. B. Bian, "Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN," in IEEE Access, vol. 8, pp. 6997-7004, 2020

[3]. S. Ren, K. He, R. Girshick and J. Sun, "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

[4]. F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta and P. Popovski, "Five disruptive technology directions for 5G," in IEEE Communications Magazine, vol. 52, no. 2, pp. 74-80, February 2014

[5]. S. Kumar, A. S. Dixit, R. R. Malekar, H. D. Raut and L. K. Shevada, "Fifth Generation Antennas: A Comprehensive Review of Design and Performance Enhancement Techniques," in IEEE Access, vol. 8, pp. 163568-163593, 2020

[6]. A. Tusha, S. Doğan and H. Arslan, "A Hybrid Downlink NOMA With OFDM and OFDM-IM for Beyond 5G Wireless Networks," in IEEE Signal Processing Letters, vol. 27, pp. 491-495, 2020

[7]. S. Ren, K. He, R. Girshick and J. Sun, "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

[8]. Y. Zhang, J. H. Han, Y. W. Kwon and Y. S. Moon, "A New Architecture of Feature Pyramid Network for Object Detection," 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020, pp. 1224-1228

[9]. K. He, X. Zhang, S. Ren, et al. “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

[10]. T. -Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie, "Feature Pyramid Networks for Object Detection," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936-944,

[11]. M. Overgaard Lauersen, B. Köylü, B. Haddock and J. A. Sorensen, "Kidney segmentation for quantitative analysis applying MaskRCNN architecture," 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1-6

[12]. Songhui, S. Mingming and H. Chufeng, "Objects detection and location based on mask RCNN and stereo vision," 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2019, pp. 369-373

[13]. X. Siheng et al., "Power Equipment Recognition Method based on Mask R-CNN and Bayesian Context Network," 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020, pp. 1-5

Cite this article

Li,H.;Palacin,R.;Dlay,S. (2023). An innovative application of pantograph recognition system based on deep learning. Applied and Computational Engineering,6,827-833.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. R. Girshick, “Fast r-cnn”, Proceedings of the IEEE International Conference on Computer Vision, pp.1440 – 1448, 2015.

[2]. Q. Zhang, X. Chang and S. B. Bian, "Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN," in IEEE Access, vol. 8, pp. 6997-7004, 2020

[3]. S. Ren, K. He, R. Girshick and J. Sun, "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

[4]. F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta and P. Popovski, "Five disruptive technology directions for 5G," in IEEE Communications Magazine, vol. 52, no. 2, pp. 74-80, February 2014

[5]. S. Kumar, A. S. Dixit, R. R. Malekar, H. D. Raut and L. K. Shevada, "Fifth Generation Antennas: A Comprehensive Review of Design and Performance Enhancement Techniques," in IEEE Access, vol. 8, pp. 163568-163593, 2020

[6]. A. Tusha, S. Doğan and H. Arslan, "A Hybrid Downlink NOMA With OFDM and OFDM-IM for Beyond 5G Wireless Networks," in IEEE Signal Processing Letters, vol. 27, pp. 491-495, 2020

[7]. S. Ren, K. He, R. Girshick and J. Sun, "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

[8]. Y. Zhang, J. H. Han, Y. W. Kwon and Y. S. Moon, "A New Architecture of Feature Pyramid Network for Object Detection," 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020, pp. 1224-1228

[9]. K. He, X. Zhang, S. Ren, et al. “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

[10]. T. -Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie, "Feature Pyramid Networks for Object Detection," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936-944,

[11]. M. Overgaard Lauersen, B. Köylü, B. Haddock and J. A. Sorensen, "Kidney segmentation for quantitative analysis applying MaskRCNN architecture," 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1-6

[12]. Songhui, S. Mingming and H. Chufeng, "Objects detection and location based on mask RCNN and stereo vision," 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2019, pp. 369-373

[13]. X. Siheng et al., "Power Equipment Recognition Method based on Mask R-CNN and Bayesian Context Network," 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020, pp. 1-5

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