Abstract
To ensure the safe operation of the train in metro system, catenary anomaly detection and alerting security have become a major issue to be resolved. Moreover, effective pantograph detection is an important foundation of catenary anomaly detection. In this paper, we present a novel computer vision pantograph detection system involving Faster R-CNN object detection method. Based on the architecture of deep Convolution Neural Network (CNN), we modify the Faster R-CNN to real-time detect the subway pantograph. It combines region proposal generation with object detection. The results reveal that the approach achieves inspiring detection accuracy with over 94.9%. The system can work in different environment of the subway’s train, at different times throughout the day. It provides important reference for subsequent anomaly detection of catenary.
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Acknowledgments
The authors greatly appreciate the financial supports of National Science Foundation of China (No. 61370174).
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Ge, R., Zhu, Y., Xiao, Y., Chen, Z. (2017). The Subway Pantograph Detection Using Modified Faster R-CNN. In: Yang, X., Zhai, G. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2016. Communications in Computer and Information Science, vol 685. Springer, Singapore. https://doi.org/10.1007/978-981-10-4211-9_20
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DOI: https://doi.org/10.1007/978-981-10-4211-9_20
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