• Infrared Technology
  • Vol. 43, Issue 11, 1104 (2021)
Haifeng SU, Yan ZHAO*, Zejun WU, Bo CHENG, and Linfei LYU
Author Affiliations
  • [in Chinese]
  • show less
    DOI: Cite this Article
    SU Haifeng, ZHAO Yan, WU Zejun, CHENG Bo, LYU Linfei. Refined Infrared Object Detection Model for Power Equipment Based on Improved RetinaNet[J]. Infrared Technology, 2021, 43(11): 1104 Copy Citation Text show less
    References

    [3] Jadin M S, Taib S. Recent progress in diagnosing the reliability of elec-trical equipment by using infrared thermography[J]. Infrared Physics & Technology, 2012, 55(4): 236-245.

    [8] REN Shaoqing, HE Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]// Advances in Neural Information Processing Systems, Montreal, Canada, 2015: 91-99.

    [9] Redmon J, Farhadi A. YOLOv3: An incremental improvement[J/OL].[2018-04-08]. https://arxiv.org/abs/1804.02767.

    [10] LIU W, Anguelov D, Erhan D, et al. SSD: single shot multibox detec-tor[C]// Proceedings of the European Conference on Computer Vision. Amsterdam, 2016: 21-37

    [14] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// Proceedings of the IEEE International Conference on Computer Vision, 2017: 2999-3007.

    [15] Bochkovskiy A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J/OL]. Computer Vision and Pattern Recognition, 2020, https://arxiv.org/abs/2004.10934.

    [16] Misra D. Mish: a self regularized non-monotonic neural activation func-tion[J/OL]. Computer Science, 2019, https://arxiv.org/abs/1908.08681.

    [17] LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for in-stance segmentation[C]//IEEE Conference on Computer Vision and Pat-tern Recognition, 2018: 8759-8768.

    [18] LIN T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117-2125.

    [19] Long J, Shelhamer E, Darrell T. Fully convolutional networks for se-mantic segmentation[J]. IEEE Transactions on Pattern Analysis & Ma-chine Intelligence, 2014, 39(4): 640-651.

    [20] NAIR V, HINTON G E. Rectified linear units improve restricted boltzmann machines[C]//Proceedings of the 27th International Confe-rence on Machine Learning(ICML-10), 2010: 807-814.

    [21] WEN Long, GAO Liang, LI Xinyu. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Sys-tems, Man, and Cybernetics: Systems, 2019, 49(1): 136-144.

    SU Haifeng, ZHAO Yan, WU Zejun, CHENG Bo, LYU Linfei. Refined Infrared Object Detection Model for Power Equipment Based on Improved RetinaNet[J]. Infrared Technology, 2021, 43(11): 1104
    Download Citation