• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0410020 (2021)
Li Zhang1, Danping Huang1、*, Shipeng Liao2, Shaodong Yu1、3, Jianqiu Ye1, Xin Wang1, and Na Dong1
Author Affiliations
  • 1School of Mechanical Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan 644000, China;
  • 2Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan 610041, China
  • 3School of Mechanical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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    DOI: 10.3788/LOP202158.0410020 Cite this Article Set citation alerts
    Li Zhang, Danping Huang, Shipeng Liao, Shaodong Yu, Jianqiu Ye, Xin Wang, Na Dong. Wheelset Tread Defect Detection Method Based on Target Detection Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410020 Copy Citation Text show less
    Schematic of collection equipment
    Fig. 1. Schematic of collection equipment
    Collected wheelset images
    Fig. 2. Collected wheelset images
    Flow chart of dual network detection algorithm
    Fig. 3. Flow chart of dual network detection algorithm
    Areas with contrasting gray values
    Fig. 4. Areas with contrasting gray values
    Direction-corrected wheelset images
    Fig. 5. Direction-corrected wheelset images
    SSD network structure
    Fig. 6. SSD network structure
    YOLOv3 network structure
    Fig. 7. YOLOv3 network structure
    Distribution chart between original Anchor Boxes and target size
    Fig. 8. Distribution chart between original Anchor Boxes and target size
    M-YOLOv3 network structure
    Fig. 9. M-YOLOv3 network structure
    Distribution chart between new Anchor Boxes and target size
    Fig. 10. Distribution chart between new Anchor Boxes and target size
    P-R curves of three models when extracting tread
    Fig. 11. P-R curves of three models when extracting tread
    Partial identification renderings of the three networks. (a) SSD300; (b) YOLOv3; (c) YOLOv3-tiny
    Fig. 12. Partial identification renderings of the three networks. (a) SSD300; (b) YOLOv3; (c) YOLOv3-tiny
    P-R curves of five models when identifying defects
    Fig. 13. P-R curves of five models when identifying defects
    Partial identification renderings of five kinds of networks. (a) M-YOLOv3; (b) YOLOv3; (c) YOLOv3-tiny; (d) SSD300; (e) SSD512
    Fig. 14. Partial identification renderings of five kinds of networks. (a) M-YOLOv3; (b) YOLOv3; (c) YOLOv3-tiny; (d) SSD300; (e) SSD512
    Missing and misdetection of some tread defects
    Fig. 15. Missing and misdetection of some tread defects
    P-R curve of two detection methods
    Fig. 16. P-R curve of two detection methods
    NetworkAP /%Time /msIOU /%
    SSD30099.831.883.2
    YOLOv396.944.380.1
    YOLOv3-tiny90.613.973.6
    Table 1. Tread detection performance
    NetworkM-YOLOv3YOLOv3YOLOv3-tinySSD300SSD512
    AP /%89.990.572.219.342.0
    Time /ms58.763.217.524.546.4
    Table 2. Defect detection performance
    NetworkAP /%Time /ms
    YOLOv373.292.7
    Exper1 & Exper289.792.3
    Table 3. Defect detection performance of two detection methods
    Li Zhang, Danping Huang, Shipeng Liao, Shaodong Yu, Jianqiu Ye, Xin Wang, Na Dong. Wheelset Tread Defect Detection Method Based on Target Detection Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410020
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