• Laser & Optoelectronics Progress
  • Vol. 57, Issue 14, 141503 (2020)
Liwei Dai* and Shan Huang
Author Affiliations
  • College of Electrical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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    DOI: 10.3788/LOP57.141503 Cite this Article Set citation alerts
    Liwei Dai, Shan Huang. Indices Optimizing for Object Detection in Traffic Scenes[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141503 Copy Citation Text show less
    Structure of DBL
    Fig. 1. Structure of DBL
    Structure of Res Unit
    Fig. 2. Structure of Res Unit
    Structure of Resn
    Fig. 3. Structure of Resn
    Structure of YOLOv3 DarkNet-53 baseline
    Fig. 4. Structure of YOLOv3 DarkNet-53 baseline
    Contrast schematics between IOU and GIOU
    Fig. 5. Contrast schematics between IOU and GIOU
    Schematic of Mixup interpolation
    Fig. 6. Schematic of Mixup interpolation
    Piecewise learning rate curve
    Fig. 7. Piecewise learning rate curve
    Cosine Decay learning rate curve
    Fig. 8. Cosine Decay learning rate curve
    Activation functions of Hard-Swish and Swish
    Fig. 9. Activation functions of Hard-Swish and Swish
    Effect picture of Cutout
    Fig. 10. Effect picture of Cutout
    Effect pictures of ACDC. (a) VOC dataset; (b) KITTI 2D dataset
    Fig. 11. Effect pictures of ACDC. (a) VOC dataset; (b) KITTI 2D dataset
    Comparisonsof 20 classes of AP index on VOC dataset
    Fig. 12. Comparisonsof 20 classes of AP index on VOC dataset
    Comparisons of 7 classes of AP index on KITTI 2D dataset
    Fig. 13. Comparisons of 7 classes of AP index on KITTI 2D dataset
    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
    YOLOv3 modelmAP /%Δ /%Time consuming /h
    Origin76.5522.72
    +Hard-Swish76.70+0.1523.58
    +Swish76.66+0.1125.07
    Table 1. Comparisons of Hard-Swish and Swish activation function in mAP index
    YOLOv3 modelmAP /%Δ /%FPS(416×416) /(frame/s)Time consuming /h
    Origin76.5522.72
    +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 2. Comparisons of experiment indices on VOC dataset
    YOLOv3 modelmAP /%Δ /%FPS(416×416) /(frame/s)Time consuming /h
    Origin81.9211.8
    +ACDC83.96+2.0433.4(±0.5)11.82
    +All89.21+7.2911.66
    Table 3. Comparisons of experiment indices on KITTI 2D dataset
    Liwei Dai, Shan Huang. Indices Optimizing for Object Detection in Traffic Scenes[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141503
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