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A Stackable Attention-Guided Multi-scale CNN for Number Plate Detection

  • Yixuan Wang
  • Shangdong Zheng
  • Wei Xu
  • Yang Xu
  • Tianming Zhan
  • Peng Zheng
  • Zhihui Wei
  • Zebin WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

High-speed railway transportation faces various risks due to its increasing laying area. A key to find the abnormal conditions is to locate the position of a pillar nearby. The complex background and different size of number plate (NP) makes the detection of number plate in overhead catenary system (OCS) a hard work. In this paper, we propose a novel framework and solution with two main advances: (1) a stackable attention model, which can improve the robustness of the system in complex scenarios; (2) multi-scale feature fusion stage, improve detection accuracy of distance pillar number plate. Both of them can be integrated into any CNN architectures seamlessly with negligible overheads and are end-to-end trainable along with base CNNs. We demonstrate the effectiveness of our method with experiments on different high-speed train lines and one benchmark dataset – Pascal VOC [4].

Keywords

Number plate detection Attention model Multi-scale 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yixuan Wang
    • 1
  • Shangdong Zheng
    • 1
  • Wei Xu
    • 2
  • Yang Xu
    • 1
  • Tianming Zhan
    • 1
  • Peng Zheng
    • 1
  • Zhihui Wei
    • 1
  • Zebin Wu
    • 1
    Email author
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.China Railway Shanghai Group Co., Ltd., Nanjing Power Supply SectionNangJingChina

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