• High Power Laser and Particle Beams
  • Vol. 34, Issue 3, 031023 (2022)
Chenyi Yang, Yuqing He*, Junyuan Zhao, and Guorong Li
Author Affiliations
  • Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
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    DOI: 10.11884/HPLPB202234.210335 Cite this Article
    Chenyi Yang, Yuqing He, Junyuan Zhao, Guorong Li. Lightweight neural network hand gesture recognition method for embedded platforms[J]. High Power Laser and Particle Beams, 2022, 34(3): 031023 Copy Citation Text show less
    Construction and pipeline of the algorithm
    Fig. 1. Construction and pipeline of the algorithm
    Depthwise separable convolution
    Fig. 2. Depthwise separable convolution
    Squeeze-and-excitation module[22]
    Fig. 3. Squeeze-and-excitation module[22]
    Hand gesture recognition network based on MobileNetv3-SSDLite
    Fig. 4. Hand gesture recognition network based on MobileNetv3-SSDLite
    Neural network structure before and after the embedded optimization
    Fig. 5. Neural network structure before and after the embedded optimization
    The chosen hand gestures
    Fig. 6. The chosen hand gestures
    Selecting images for hand gesture dataset
    Fig. 7. Selecting images for hand gesture dataset
    Network loss in training process
    Fig. 8. Network loss in training process
    NVIDIA Jetson TX2 embedded processor developer kit
    Fig. 9. NVIDIA Jetson TX2 embedded processor developer kit
    Part of the hand gesture recognition results
    Fig. 10. Part of the hand gesture recognition results
    network structureparams/MbyteMACs/106ImageNet accuracy/%
    VGG1613.81530071.5
    MobieNetv14.256970.6
    MobileNetv23.430072.0
    MobileNetv35.421975.2
    Table 1. MobileNet series comparison to VGG16
    extra layersshape
    layer 1$39 \times 39 \times 512$
    layer 2$19 \times 19 \times 1024$
    layer 3$10 \times 10 \times 512$
    layer 4$5 \times 5 \times 256$
    layer 5$3 \times 3 \times 256$
    layer 6$1 \times 1 \times 256$
    Table 2. Extra feature map layers for object detection
    network structureparams/MbyteMACs/106mAP/%
    SSD14.8125019.3
    SSDLite2.135022.2
    Table 3. SSDLite detection head comparison to SSD
    hand gestureaccuracy/%
    099.64
    1100.00
    399.51
    499.22
    599.69
    average99.61
    Table 4. Recognition results of hand gestures
    scenariosaverage accuracy/%
    multiple hand gestures96
    complicated background64
    low light intensity72
    Table 5. Recognition results of hand gestureson various scenarios
    algorithmparams/MbyteMACs/106frame rate/(frame/s)mean accuracy/%
    VGG16-SSD24.330654291.75
    MobieNetv1-SSD7.212991293.98
    MobileNetv1-SSDLite4.111301693.86
    MobileNetv2-SSDLite3.16563691.01
    MobileNetv3-SSDLite2.25265899.61
    Table 6. Comparison of different hand gesture recognition algorithms.
    Chenyi Yang, Yuqing He, Junyuan Zhao, Guorong Li. Lightweight neural network hand gesture recognition method for embedded platforms[J]. High Power Laser and Particle Beams, 2022, 34(3): 031023
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