• Laser & Optoelectronics Progress
  • Vol. 58, Issue 24, 2415009 (2021)
Yongfu Zhou1, Wenlong Li1、2, and Ranran Hu2、*
Author Affiliations
  • 1School of Management Engineering, Jilin Communications Polytechnic, Changchun, Jilin 130012, China
  • 2School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130022, China
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    DOI: 10.3788/LOP202158.2415009 Cite this Article Set citation alerts
    Yongfu Zhou, Wenlong Li, Ranran Hu. Two-Channel SSD Pedestrian Head Detection Algorithm Based on Multi-Scale Feature fusion[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2415009 Copy Citation Text show less
    Structure of SSD300 network
    Fig. 1. Structure of SSD300 network
    Structure of two-channel network
    Fig. 2. Structure of two-channel network
    Improved two-channel SSD network model based on feature fusion
    Fig. 3. Improved two-channel SSD network model based on feature fusion
    Partial depth images. (a) Image 1; (b) image 2; (c) image 3
    Fig. 4. Partial depth images. (a) Image 1; (b) image 2; (c) image 3
    Color images and depth images under different operations. (a) Original images; (b) 180 ° rotation; (c) x-axis reversal; (d) y-axis reversal
    Fig. 5. Color images and depth images under different operations. (a) Original images; (b) 180 ° rotation; (c) x-axis reversal; (d) y-axis reversal
    Training loss and accuracy curves. (a) Training loss curve; (b) accuracy curve
    Fig. 6. Training loss and accuracy curves. (a) Training loss curve; (b) accuracy curve
    Relationship between precision and recall of different networks
    Fig. 7. Relationship between precision and recall of different networks
    Detection results of SSD network and two-channel SSD network. (a) Original images; (b)SSD network; (c) two-channel SSD network
    Fig. 8. Detection results of SSD network and two-channel SSD network. (a) Original images; (b)SSD network; (c) two-channel SSD network
    Detection results of SSD network and improved SSD network with multi-scale feature fusion. (a) Original images; (b) SSD network; (c) two-channel SSD network
    Fig. 9. Detection results of SSD network and improved SSD network with multi-scale feature fusion. (a) Original images; (b) SSD network; (c) two-channel SSD network
    Detection results of each algorithm under different illumination variation conditions. (a) SSD algorithm; (b) DSSD algorithm; (c) improved algorithm
    Fig. 10. Detection results of each algorithm under different illumination variation conditions. (a) SSD algorithm; (b) DSSD algorithm; (c) improved algorithm
    Detection results of each algorithm under different occlusion conditions. (a) SSD algorithm; (b) DSSD algorithm; (c) improved algorithm
    Fig. 11. Detection results of each algorithm under different occlusion conditions. (a) SSD algorithm; (b) DSSD algorithm; (c) improved algorithm
    ParameterMDConv 4_3_fusionFC 7_fusionConv 8_2_fusion
    Feature map size/(pixel×pixel)38×3819×1910×10
    Number of default boxes463
    ar{1,1,2,1/2}{1,1,2,1/2,3,1/3}{1,2,1/2}
    Small side length3060111
    Large side length60111-
    Table 1. Size of default box for each layer
    NetworkmAP/%FPS
    Simplified SSD network83.7033
    SSD network84.9029
    Table 2. Average accuracy and speed of target detection in different prior frame networks
    Fusion modeWeight ratioRGB imageDepth imageTwo-channel SSD
    Concat83.7082.6189.86
    Eltwise
    0.7∶0.393.59
    0.5∶0.591.94
    0.3∶0.787.46
    Table 3. Average detection accuracy of two-channel SSD network unit: %
    ParameterSSDDSSDMulti-scale SSD
    mAP83.7088.4391.47
    Table 4. Average detection accuracy of multi-scale feature fusion network unit: %
    ParameterSSDRGB-D+YOLOv2RGB-D+Faster-RCNNRef. [18]Ref. [19]Ref. [25]Proposed model
    mAP84.9092.9593.1492.0190.7895.4797.80
    Table 5. Average detection accuracy of different models unit: %
    Yongfu Zhou, Wenlong Li, Ranran Hu. Two-Channel SSD Pedestrian Head Detection Algorithm Based on Multi-Scale Feature fusion[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2415009
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