A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform

Donglian QI; Jiaying QIAN; Yunfeng YAN; Xiaohong ZENG

doi:10.11999/JEIT200357

Volume 43 Issue 7

Jul. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(7): 2014-2022

Donglian QI, Jiaying QIAN, Yunfeng YAN, Xiaohong ZENG. A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2014-2022. doi: 10.11999/JEIT200357

Citation:

Donglian QI, Jiaying QIAN, Yunfeng YAN, Xiaohong ZENG. A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2014-2022. doi: 10.11999/JEIT200357

Citation:

Donglian QI, Jiaying QIAN, Yunfeng YAN, Xiaohong ZENG. A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2014-2022. doi: 10.11999/JEIT200357

PDF( 2206 KB)

A Multi-scale Detection Method for Dropper States in High-speed Railway Contact Network Based on RefineDet Network and Hough Transform

doi: 10.11999/JEIT200357

1.
Zhejiang University, Hangzhou 310027, China
2.
Southwest Jiaotong University, Chengdu 611756, China

Funds: The Key Research and Development Plan of Zhejiang Province (2019C01001), The National Youth Science Fund Project (62001416), The Fundamental Research Funds for the Central Universities (2018FZA122)

Received Date: 2020-05-08
Rev Recd Date: 2021-02-21

Available Online: 2021-03-30

Publish Date: 2021-07-10

Abstract

Abstract

In order to solve the problems of detection and state analysis of high-speed railway catenary droppers, this paper proposes a multi-scale detection method for dropper states based on Refinedet network and Hough transform. First, the positioning result of droppers through Refinedet network is obtained, and Hough transform is used to locate where the dropper line is; Then the surrounding area of the dropper line is extracted with a ralated twiddle factor. Those extracted areas, replacing the results of detection net, are fed into classification network for training the final dropper state analysis mode. Experiments show that the accuracy of dropper detection model is over 95.3%, and the dropper state analysis model can eliminate the impact of meaningless area pixels while accelerating training process, the final state analysis model achieves a high accuracy over 97.5%.
- Target detection,
- Deep learning,
- Contact network 4C,
- Defect analysis,
- Hough transform

FullText(HTML)

References(21)

References

[1]	LIU Xiaomin, WU Junyong, YANG Yuan, et al. Multiobjective optimization of preventive maintenance schedule on traction power system in high-speed railway[C]. 2009 Annual Reliability and Maintainability Symposium, Fort Worth, USA, 2009: 365–370.
[2]	SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. 2014 Computer Vision and Pattern Recognition, Columbus, USA, 2014.
[3]	LAW H and DENG Jia. CornerNet: Detecting objects as paired keypoints[C]. 15th European Conference on Computer Vision, Munich, Germany, 2018: 765–781.
[4]	ZHOU Xingyi, WANG Dequan, KRÄHENBUHL P, et al. Objects as points[J]. arXiv: 1904.07850, 2019.
[5]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
[6]	GIRSHICK R. Fast R-CNN[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.
[7]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 91–99.
[8]	HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980–2988.
[9]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788.
[10]	REDMON J and FARHADI A. YOLO9000: Better, faster, stronger[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 6517–6525.
[11]	REDMON J and FARHADI A. YOLOv3: An incremental improvement[J]. arXiv: 1804.02767, 2018.
[12]	LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]. 14th European Conference on Computer Vision, Amsterdam, Holland, 2016: 21–37.
[13]	FU Chengyang, LIU Wei, RANGA A, et al. DSSD: Deconvolutional single shot detector[J]. arXiv: 1701.06659, 2017.
[14]	刘凯, 刘志刚, 陈隽文. 基于加速区域卷积神经网络的高铁接触网承力索底座裂纹检测研究[J]. 铁道学报, 2019, 41(7): 43–49. doi: 10.3969/j.issn.1001-8360.2019.07.006 LIU Kai, LIU Zhigang, and CHEN Junwen. Crack detection of messenger wire supporter in catenary support devices of high-speed railway based on faster R-CNN[J]. Journal of the China Railway Society, 2019, 41(7): 43–49. doi: 10.3969/j.issn.1001-8360.2019.07.006
[15]	李彩林, 张青华, 陈文贺, 等. 基于深度学习的绝缘子定向识别算法[J]. 电子与信息学报, 2020, 42(4): 1033–1040. doi: 10.11999/JEIT190350 LI Cailin, ZHANG Qinghua, CHEN Wenhe, et al. Insulator orientation detection based on deep learning[J]. Journal of Electronics &Information Technology, 2020, 42(4): 1033–1040. doi: 10.11999/JEIT190350
[16]	杨红梅, 刘志刚, 韩烨, 等. 基于快速鲁棒性特征匹配的电气化铁路绝缘子不良状态检测[J]. 电网技术, 2013, 37(8): 2297–2302. doi: 10.13335/j.1000-3673.pst.2013.08.038 YANG Hongmei, LIU Zhigang, HAN Ye, et al. Defective condition detection of insulators in electrified railway based on feature matching of speeded-up robust features[J]. Power System Technology, 2013, 37(8): 2297–2302. doi: 10.13335/j.1000-3673.pst.2013.08.038
[17]	闵锋, 郎达, 吴涛. 基于语义分割的接触网开口销状态检测[J]. 华中科技大学学报: 自然科学版, 2020, 48(1): 77–81. doi: 10.13245/j.hust.200114 MIN Feng, LANG Da, and WU Tao. The state detection of split pin in overhead contact system based on semantic segmentation[J]. Journal of Huazhong University of Science and Technology:Natural Science Edition, 2020, 48(1): 77–81. doi: 10.13245/j.hust.200114
[18]	ZHANG Shifeng, WEN Longyin, BIAN Xiao, et al. Single-shot refinement neural network for object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 4203–4212.
[19]	IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015: 448–456.
[20]	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 936–944.
[21]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.