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ArcMask: a robust and fast image-based method for high-speed railway pantograph-catenary arcing instance segmentation

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Abstract

The pantograph-catenary arcing reflects the health of pantograph-catenary and current collection quality of high-speed railway, so the arc detection is of great significance. However, due to the scene complexity, intra-class polymorphism and inter-class similarity of arcing and the fast running speed of high-speed railway, it is still a huge challenge to achieve fine and robust arcing detection. To overcome these issues, a robust and fast image-based instance segmentation method called ArcMask is proposed to detect pantograph-catenary arcing, which designs a new attention-based multi-scale feature fusion module that combines both top-down and down-up modules to realize arcing pixel-level instance segmentation. The effective combination of instance-level information and bottom-level semantic information balances features representation ability of top-level and bottom-level features. Compared with other instance segmentation methods (e.g., BlendMask), it can effectively learn feature representation with tiny, irregular and complex arc features and speeds up the calculation. In addition, both deformable convolution and depth-wise separable convolution are introduced in ArcMask, which aims to improve the segmentation performance of irregular arcing and efficiency. The ArcMask can distinguish different arcing instances at pixel-level with fine granularity and distinguish inter-class and intra-class features of arcing, instead of just focusing on rectangular bounding box. Experiments on self-collected dataset IVAIS-PCA2021 verify the effectiveness and efficiencies of the ArcMask. Its AP, AP50 and AP75 are 56.61, 94.14 and 64.56, respectively, and the fastest reasoning speed based on MobileNet is 56 FPS. Compared with other state-of-the-art segmentation methods, the proposed ArcMask has better integrity in arcing edge detection.

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Data availability statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 52277127), Science and Technology Innovation Talent Project of Sichuan Province (Grant No. 2021JDRC0012), Independent Research Project of National Key Laboratory of Traction Power of China (Grant No. 2019TPL-T19), Key Interdisciplinary Basic Research Project of Southwest Jiaotong University (Grant No. 2682021ZTPY089), Open Research Project of National Rail Transit Electrification and Automation Engineering Technology Research Center and Chengdu Guojia Electrical Engineering Co., Ltd (Grant No. NEEC-2019-B06), and State Scholarship Fund of China Scholarship Council. (Grant No. 202007000101).

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Authors and Affiliations

  1. Engineering Technology Research Center, National Rail Transit Electrification and Automation, Southwest Jiaotong University, Chengdu, Sichuan, China

    Wei Quan & Xuemin Lu

  2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China

    Wei Quan, Shaopeng Guo, Xuemin Lu, Guoxin Gu, Wenjing Cheng & Long Yu

  3. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan, China

    Ning Zhou

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  1. Wei Quan
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Correspondence to Xuemin Lu.

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Quan, W., Guo, S., Lu, X. et al. ArcMask: a robust and fast image-based method for high-speed railway pantograph-catenary arcing instance segmentation. Neural Comput & Applic 35, 6875–6890 (2023). https://doi.org/10.1007/s00521-022-08059-7

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