Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2215008(2024)

Prohibited Item Detection Method of X-Ray Security Inspection Image Based on Improved YOLOv8s

Jiaxin Dong1, Ting Luo1、*, Gen Li1, Xing Zhao2,3, and Yunsong Zhao2,3
Author Affiliations
  • 1School of Information Network Security, People's Public Security University of China, Beijing 100038, China
  • 2School of Mathematical Sciences, Capital Normal University, Beijing 100048, China
  • 3Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
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    Figures & Tables(17)
    Structure of YOLOv8 model
    Structure diagram of ODConv
    Structure diagrams of different feature fusion networks. (a) Structure of FPN; (b) structure of PAN; (c) structure of BiFPN; (d) structure of BiFPN in YOLOv8s-BiOG
    Structure of GAM
    Channel attention submodule
    Spatial attention submodule
    Structure of YOLOv8s-BiOG model
    Re-annotation of partial images from PIDray dataset
    Statistics on the number of prohibited items in SI2Pxray dataset
    Random X-ray images of SI2Pxray dataset and OPIXray dataset. (a)‒(c) SIXray dataset; (d)‒(f) PIDray dataset; (g)‒(i) OPIXray dataset
    DIoU definition diagram
    Confusion matrix corresponding to 5 prohibited items in SI2Pxray dataset. (a) Confusion matrix of YOLOv8s; (b) confusion matrix of YOLOv8s-BiOG
    Comparison of training process. (a) Precison comparison; (b) recall comparison; (c) mAP comparison
    Comparison images of detection results. (a)‒(e) Detection results of YOLOv8s; (f)‒(j) detection results of YOLOv8s-BiOG
    • Table 1. Performance comparison of different models on SI2Pxray dataset

      View table

      Table 1. Performance comparison of different models on SI2Pxray dataset

      ModelAP /%mAP /%Size /MBDetect rate /(ms/frame)
      gunknifewrenchpliersscissors
      Faster R-CNN89.581.680.184.485.284.2108.158.5
      SSD88.274.071.377.284.879.193.815.3
      YOLOv3-tiny90.885.284.991.891.288.824.42.1
      YOLOv5s91.588.387.692.592.490.514.02.7
      YOLOv6s91.086.888.992.093.390.432.82.7
      YOLOv8s91.789.586.993.093.590.922.52.5
      YOLOv8s-BiOG92.491.891.895.196.193.440.62.9
    • Table 2. Performance comparison of different models on OPIXray dataset

      View table

      Table 2. Performance comparison of different models on OPIXray dataset

      ModelAP /%mAP /%
      straight knifefolding knifescissorsutility knifemulti-tool knife
      YOLOv3-tiny63.286.698.880.190.883.9
      YOLOv5s78.593.798.684.294.289.8
      YOLOv6s77.091.597.983.794.688.9
      YOLOv8s76.891.298.186.495.889.7
      YOLOv8s-BiOG82.494.098.788.695.291.8
    • Table 3. Ablation experiments

      View table

      Table 3. Ablation experiments

      ModelPrecision /%Recall /%mAP /%GFLOPs /109Detect rate /(ms/frame)
      YOLOv8s93.284.590.928.42.5
      YOLOv8s+ODConv94.285.891.823.82.6
      YOLOv8s+BiFPN94.586.091.728.42.5
      YOLOv8s+GAM93.985.892.132.62.9
      YOLOv8s+ODConv+BiFPN94.686.892.424.12.6
      YOLOv8s+BiFPN+GAM94.087.192.733.02.9
      YOLOv8s+ODConv+GAM95.186.792.928.02.9
      YOLOv8s+BiFPN+ODConv+GAM95.588.693.428.32.9
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    Jiaxin Dong, Ting Luo, Gen Li, Xing Zhao, Yunsong Zhao. Prohibited Item Detection Method of X-Ray Security Inspection Image Based on Improved YOLOv8s[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2215008

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    Paper Information

    Category: Machine Vision

    Received: Feb. 18, 2024

    Accepted: Apr. 11, 2024

    Published Online: Nov. 19, 2024

    The Author Email: Ting Luo (luoting@ppsuc.edu.cn)

    DOI:10.3788/LOP240698

    CSTR:32186.14.LOP240698

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