• Laser & Optoelectronics Progress
  • Vol. 58, Issue 2, 0210022 (2021)
Shaoqing Yao1 and Zhigang Su1、2、*
Author Affiliations
  • 1Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
  • 2Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China
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    DOI: 10.3788/LOP202158.0210022 Cite this Article Set citation alerts
    Shaoqing Yao, Zhigang Su. Prohibited Item Identification Algorithm Based onLightweight Segmentation Network[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210022 Copy Citation Text show less
    Prohibited item identification model
    Fig. 1. Prohibited item identification model
    Results of data augmentation
    Fig. 2. Results of data augmentation
    Dilated convolution module. (a) Without feature multiplexing operation; (b) with 3-layer convolution; (c) with 2-layer convolution
    Fig. 3. Dilated convolution module. (a) Without feature multiplexing operation; (b) with 3-layer convolution; (c) with 2-layer convolution
    Asymmetric convolution module. (a) Downsampling; (b) upsampling
    Fig. 4. Asymmetric convolution module. (a) Downsampling; (b) upsampling
    Example of images in dataset
    Fig. 5. Example of images in dataset
    Segmentation results of contrast experiment
    Fig. 6. Segmentation results of contrast experiment
    Segmentation results of ablation experiment
    Fig. 7. Segmentation results of ablation experiment
    AlgorithmmIoU /10-2Time /msFPS
    FCN8s62.151208.3
    PSPNet70.561526.5
    Deeplabv371.151626.1
    An's model74.512763.6
    ENet51.217128.7
    Proposed algorithm73.183627.1
    Table 1. Results of contrast experiment
    AlgorithmmIoU /10-2Time /msFPS
    Group 163.286116.3
    Group 269.758411.8
    Group 368.305817
    Group 467.373329.5
    Group 567.844025
    Proposed algorithm73.183627.1
    Table 2. Results of ablation experiment
    Shaoqing Yao, Zhigang Su. Prohibited Item Identification Algorithm Based onLightweight Segmentation Network[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210022
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