• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 22, Issue 11, 1221 (2024)
LIANG Nan1, ZHANG Wei1,*, LIU Yanglong2, and JING Hailin1
Author Affiliations
  • 1Institute of Applied Physics Co., Ltd, Henan Academy of Sciences, Zhengzhou Henan 450000, China
  • 2College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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    DOI: 10.11805/tkyda2023253 Cite this Article
    LIANG Nan, ZHANG Wei, LIU Yanglong, JING Hailin. Fastener clips defect detection based on improved Faster R-CNN in high-speed railway[J]. Journal of Terahertz Science and Electronic Information Technology , 2024, 22(11): 1221 Copy Citation Text show less
    References

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    [2] AYTEKIN C, REZAEITABAR Y, DOGRU S, et al. Railway fastener inspection by real-time machine vision[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(7): 1101-1107. doi: 10.1109/TSMC.2014.2388435.

    [3] PENG Zhiyong, WANG Chao, MA Ziji, et al. A multifeature hierarchical locating algorithm for hexagon nut of railway fasteners[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(3): 693-699. doi: 10.1109/TIM.2019.2905309.

    [4] LIU J W, LIU H L, CHAKRABORTY C, et al. Cascade learning embedded vision inspection of rail fastener by using a fault detection IoT vehicle[J]. IEEE Internet of Things Journal, 2023, 10(4): 3006-3017. doi: 10.1109/JIOT.2021.3126875.

    [5] TU Zhenwei, WU Songrong, KANG Gaoqiang, et al. Real-time defect detection of track components: considering class imbalance and subtle difference between classes[J]. IEEE Transactions on Instrumentation and Measurement, 2021(70): 5017712-1-12. doi: 10.1109/TIM.2021.3117357.

    [6] FENG Hao, JIANG Zhiguo, XIE Fengying, et al. Automatic fastener classification and defect detection in vision-based railway inspection systems[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(4): 877-888. doi: 10.1109/TIM.2013.2283741.

    [7] LIU Jianwei, TENG Yun, SHI Bo, et al. A hierarchical learning approach for railway fastener detection using imbalanced samples[J]. Measurement, 2021(186): 110240. doi: 10.1016/j.measurement.2021.110240.

    [8] LIU Jianwei, TENG Yun, NI Xuefeng, et al. A fastener inspection method based on defective sample generation and deep convolutional neural network[J]. IEEE Sensors Journal, 2021, 21(10): 12179-12188. doi: 10.1109/JSEN.2021.3062021.

    [9] LIU Jianwei, MA Ziji, QIU Yuan, et al. Four discriminator cycle-consistent adversarial network for improving railway defective fastener inspection[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 10636-10645. doi: 10.1109/TITS.2021.3095167.

    [10] LIU Junbo, HUANG Yaping, ZOU Qi, et al. Learning visual similarity for inspecting defective railway fasteners[J]. IEEE Sensors Journal, 2019, 19(16): 6844-6857. doi: 10.1109/JSEN.2019.2911015.

    [11] WEI Xiukun, YANG Ziming, LIU Yuxin, et al. Railway track fastener defect detection based on image processing and deep learning techniques: a comparative study[J]. Engineering Applications of Artificial Intelligence, 2019(80): 66-81. doi: 10.1016/j.engappai.2019.01.008.

    [12] WEI Dehua, WEI Xiukun, TANG Qingfeng, et al. RTLSeg: a novel multi-component inspection network for railway track line based on instance segmentation[J]. Engineering Applications of Artificial Intelligence, 2023(119): 105822. doi: 10.1016/j.engappai.2023.105822.

    [13] QI Hangyu, XU Tianhua, WANG Guang, et al. MYOLOv3-Tiny: a new convolutional neural network architecture for real-time detection of track fasteners[J]. Computers in Industry, 2020(123): 103303. doi: 10.1016/j.compind.2020.103303.

    LIANG Nan, ZHANG Wei, LIU Yanglong, JING Hailin. Fastener clips defect detection based on improved Faster R-CNN in high-speed railway[J]. Journal of Terahertz Science and Electronic Information Technology , 2024, 22(11): 1221
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