13th Asia Pacific Transportation Development Conference
An Early Warning Method of Pantograph Horn Drilling Based on Superpixel HOG Algorithm and YOLOv3 Smart Detector
Publication: Resilience and Sustainable Transportation Systems
ABSTRACT
To solve the contact net drilling problem caused by horn intrusion during pantograph operation, a warning method for the target of pantograph arch is proposed. Firstly, the method performs the target feature area on the received pantograph images by clustering algorithm to realize the target image feature extraction and the pantograph outline feature extraction, and through the clustered contour feature to obtain the location of the largest contact point area of the pantograph and catenary. Then, the target feature area is segmented from the background by super-pixel HOG target segmentation algorithm, and the target data set is thus formed by the labeled maximum feature images. Finally, The YOLOv3 smart detector is adopted to build classification model. The results showed that the proposed method could accurately track and extract the contact area of the pantograph and the catenary from the video, and had an effective significance for early warning of the pantograph drilling problem.
Get full access to this article
View all available purchase options and get full access to this chapter.
ACKNOWLEDGEMENT
Shanghai Local University Capacity Building Project (Grant No. 18030501300) Shanghai University of Engineering and Science Research Startup Project Fund (Grant No. 0240-E3-0507-19-05081) Dynamic Diagnosis Method and Theoretical Study of Vibration and Visual Fusion Characterize Track Diseases supported by NSFC (Grant No. 51975347)
REFERENCES
Landi, A., Menconi, L., & Sani, L. (2006). “Hough Transform and Thermo-Visionfor Monitoring Pantograph-Catenary System”. Journal of Micromechanics and Microengineering, 15, 1797-803.
Karakose, E., Gencoglu, M. T., Karakose, M., Aydin, I., & Akin, E. (2016). “A New Experimental Approach Using Image Processing Based Tracking for an Efficient Fault Diagnosis in Pantograph-Catenary Systems”. IEEE Transactions on Industrial Informatics, 1-1.
Takens, F. (2012). “Detecting Strange Attractors in Turbulence”. J. Proc. Dynamical Systems and Turbulence, 366-381.
Aydin, I., Karakose, M., & Akin, E. (2012). “A New Contactless Fault Diagnosis Approach for Pantograph-Catenary System”. IEEE International Conference On M echatronika, 1-6.
Aydin, I., Karakose, M., & Akin, E. (2013). “A Robust Anomaly Detection in Pantograph-Catenary System Based on Mean-Shift Tracking and Foreground Detection”. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4444-4449.
Aydin, I., Karakose, M., & Akin, E. (2012). “A new contactless fault diagnosis approach for pantograph-catenary system”. IEEE 15th International Symposium on MECHATRONIKA, Czech Republic, 1-6.
Swift, M., Aurisicchio, G., & Pace, P. (2011). “New practices for railway condition monitoring and predictive analysis”. J. Proc. the IET Conf. on Railway Condition Monitoring and Non-Destructive Testing, RCM, 1-6.
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks”. Eprint Arxiv.
Girshick, R. (2015). “Fast R-CNN”. IEEE International Conference on Computer Vision, 1440-1448.
Changalasetty, S. B., Badawi, A. S., & Ghribi, W. (2014). “Identification and feature extraction of moving vehicles in LabVIEW”. J. International Conference on Communication and Signal Processing, 19-20.
Barmada, S., Raugi, M., Tucci, M., & Romano, F. (2013). “Arc detection in pantograph-catenary systems by the use of support vector machines-based classification”. J. IET Electrical Systems in Transportation, 1-8.
Chiu, S. H., Wen, C. Y., Lee, J. H., Lin, K. H., & Chen, H. M. (2012). “A fast randomized generalized hough transform for arbitrary shape detection”. J. International Journal of Innovative Computing, Information and Control, 1103-1116.
Rusu-Anghel, S., Miklos, C., Topor, M., Demian, D., & Mezinescu, S. (2011). “Pantograph catenary system control using elements of chaos theory”. J. Pantograph Catenary Interaction Framework for Intelligent Control (PACIFIC), 1-4.
Zhu, X. H., Gao, X. R., Wang, Z. Y., Wang, L., & Yang, K. (2010). “Study on the edge detection and extraction algorithm in the pantographslipper's abrasion”. J. International Conference on Computational and Information Sciences, 474- 477.
Information & Authors
Information
Published In
Resilience and Sustainable Transportation Systems
Pages: 403 - 415
Editors: Fengxiang Qiao, Ph.D., Texas Southern University, Yong Bai, Ph.D., Marquette University, Pei-Sung Lin, Ph.D., University of South Florida, Steven I Jy Chien, Ph.D., New Jersey Institute of Technology, Yongping Zhang, Ph.D., California State Polytechnic University, and Lin Zhu, Ph.D., Shanghai University of Engineering Science
ISBN (Online): 978-0-7844-8290-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Jun 29, 2020
Published in print: Jun 29, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.