• Laser & Optoelectronics Progress
  • Vol. 58, Issue 14, 1415004 (2021)
Bin Li, Cheng Wang*, Jing Wu, Jichao Liu, Lijia Tong, and Zhenping Guo
Author Affiliations
  • Fundamentals Department, Air Force Engineering University, Xi’an, Shaanxi 710038, China
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    DOI: 10.3788/LOP202158.1415004 Cite this Article Set citation alerts
    Bin Li, Cheng Wang, Jing Wu, Jichao Liu, Lijia Tong, Zhenping Guo. Surface Defect Detection of Aeroengine Components Based on Improved YOLOv4 Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1415004 Copy Citation Text show less
    References

    [1] Kuang K J. Research on deep learning and its application on the defects detection for aero engine[D](2017).

    [2] Li S B, Yang J, Wang Z et al. Review of development and application of defect detection technology[J]. Acta Automatica Sinica, 46, 2319-2336(2020).

    [3] Tao X, Hou W, Xu D. A survey of surface defect detection methods based on deep learning[J]. Acta Automatica Sinica, 47, 1017-1034(2021).

    [4] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

    [5] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 779-788(2016).

    [6] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

    [7] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2020-11-05]. https://arxiv.org/abs/2004.10934

    [8] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

    [9] Girshick R. Fast R-CNN[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1440-1448(2015).

    [10] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2015).

    [11] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2980-2988(2017).

    [12] Zhang G S, Ge G Y, Zhu R H et al. Gear defect detection based on the improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 57, 121009(2020).

    [13] Zhou J, Jing J F, Zhang H H et al. Real time fabric defect detection method based on S-YOLOv3[J]. Laser & Optoelectronics Progress, 57, 161001(2020).

    [14] Sun Y C, Pan S G, Zhao T et al. Traffic light detection based on optimized YOLOv3 algorithm[J]. Acta Optica Sinica, 40, 1215001(2020).

    [15] Zhao Y. Research on blade damage identification method based on convolution neural network[D](2019).

    [16] Li H. Research on aeroengine blade crack detection based on image recognition[D](2019).

    [17] Li Y D, Han Z Q, Xu H Y et al. YOLOv3-lite: a lightweight crack detection network for aircraft structure based on depthwise separable convolutions[J]. Applied Sciences, 9, 3781(2019).

    [18] Chen W, Liang C H. Defect detection of aircraft engine internal convex based on SSD algorithms[J]. Electronic Measurement Technology, 43, 29-34(2020).

    Bin Li, Cheng Wang, Jing Wu, Jichao Liu, Lijia Tong, Zhenping Guo. Surface Defect Detection of Aeroengine Components Based on Improved YOLOv4 Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1415004
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