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Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141503(2020)

Indices Optimizing for Object Detection in Traffic Scenes

Liwei Dai* and Shan Huang
Author Affiliations
  • College of Electrical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (17)
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    References (15)
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    Figures & Tables(17)
    Structure of DBL

    Fig. 1. Structure of DBL

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    Structure of Res Unit

    Fig. 2. Structure of Res Unit

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    Structure of Resn

    Fig. 3. Structure of Resn

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    Structure of YOLOv3 DarkNet-53 baseline

    Fig. 4. Structure of YOLOv3 DarkNet-53 baseline

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    Contrast schematics between IOU and GIOU

    Fig. 5. Contrast schematics between IOU and GIOU

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    Schematic of Mixup interpolation

    Fig. 6. Schematic of Mixup interpolation

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    Piecewise learning rate curve

    Fig. 7. Piecewise learning rate curve

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    Cosine Decay learning rate curve

    Fig. 8. Cosine Decay learning rate curve

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    Activation functions of Hard-Swish and Swish

    Fig. 9. Activation functions of Hard-Swish and Swish

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    Effect picture of Cutout

    Fig. 10. Effect picture of Cutout

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    Effect pictures of ACDC. (a) VOC dataset; (b) KITTI 2D dataset

    Fig. 11. Effect pictures of ACDC. (a) VOC dataset; (b) KITTI 2D dataset

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    Comparisonsof 20 classes of AP index on VOC dataset

    Fig. 12. Comparisonsof 20 classes of AP index on VOC dataset

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    Comparisons of 7 classes of AP index on KITTI 2D dataset

    Fig. 13. Comparisons of 7 classes of AP index on KITTI 2D dataset

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    Comparisons of detection results of different models in traffic scenes. (a) Origin model; (b) +ACDC model; (c) +All model

    Fig. 14. Comparisons of detection results of different models in traffic scenes. (a) Origin model; (b) +ACDC model; (c) +All model

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    • Table 1. Comparisons of Hard-Swish and Swish activation function in mAP index

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      Table 1. Comparisons of Hard-Swish and Swish activation function in mAP index

      YOLOv3 modelmAP /%Δ /%Time consuming /h
      Origin76.55—22.72
      +Hard-Swish76.70+0.1523.58
      +Swish76.66+0.1125.07

    • Table 2. Comparisons of experiment indices on VOC dataset

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      Table 2. Comparisons of experiment indices on VOC dataset

      YOLOv3 modelmAP /%Δ /%FPS(416×416) /(frame/s)Time consuming /h
      Origin76.55—22.72
      +Cosine Decay77.21+0.6622.73
      +ACDC78.19+1.6422.78
      +Mixup77.88+1.3332.2(±0.5)22.77
      +Focal Loss81.02+4.4720.2
      +GIOU77.75+1.222.75
      +Hard-Swish76.7+0.1523.58
      +All84.21+7.6622.48

    • Table 3. Comparisons of experiment indices on KITTI 2D dataset

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      Table 3. Comparisons of experiment indices on KITTI 2D dataset

      YOLOv3 modelmAP /%Δ /%FPS(416×416) /(frame/s)Time consuming /h
      Origin81.92—11.8
      +ACDC83.96+2.0433.4(±0.5)11.82
      +All89.21+7.2911.66
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    Liwei Dai, Shan Huang. Indices Optimizing for Object Detection in Traffic Scenes[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141503

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    Paper Information

    Category: Machine Vision

    Received: Oct. 9, 2019

    Accepted: Dec. 11, 2019

    Published Online: Jul. 28, 2020

    The Author Email: Liwei Dai (3376321599@qq.com)

    DOI:10.3788/LOP57.141503

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology