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结合空洞卷积和迁移学习改进YOLOv4的X光安检危险品检测

吴海滨 魏喜盈 刘美红 王爱丽 刘赫 岩堀祐之

吴海滨, 魏喜盈, 刘美红, 王爱丽, 刘赫, 岩堀祐之. 结合空洞卷积和迁移学习改进YOLOv4的X光安检危险品检测[J]. 中国光学, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078
引用本文: 吴海滨, 魏喜盈, 刘美红, 王爱丽, 刘赫, 岩堀祐之. 结合空洞卷积和迁移学习改进YOLOv4的X光安检危险品检测[J]. 中国光学, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078
WU Hai-bin, WEI Xi-ying, LIU Mei-hong, WANG Ai-li, LIU He, IWAHORI Yu-ji. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078
Citation: WU Hai-bin, WEI Xi-ying, LIU Mei-hong, WANG Ai-li, LIU He, IWAHORI Yu-ji. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078

结合空洞卷积和迁移学习改进YOLOv4的X光安检危险品检测

doi: 10.37188/CO.2021-0078
基金项目: 国家自然基金科学基金(No. 61671190, No. 61801149);JSPS科学基金(No. #20K11873)
详细信息
    作者简介:

    吴海滨(1977—),男,上海人,博士,教授(博导),2002年于哈尔滨工业大学获得硕士学位,2008年于哈尔滨理工大学获得博士学位,现为哈尔滨理工大学测控技术与通信工程学院教授,主要从事机器视觉、医学虚拟现实、深度学习图像分类研究。E-mail:woo@hrbust.edu.cn

    王爱丽(1979—),女,天津人,博士,副教授,硕士生导师,2008年于哈尔滨工业大学获得博士学位,现为哈尔滨理工大学测控技术与通信工程学院副教授,主要从事机器视觉、深度学习图像分类研究。E-mail:aili925@hrbust.edu.cn

  • 中图分类号: TP391.4;TH691.9

Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning

Funds: Supported by National Natural Science Foundation of China (No. 61671190, No. 61801149); Japan Society for the Promotion of Science (No. #20K11873)
More Information
  • 摘要: 由于X光安检图像存在背景复杂,重叠遮挡现象严重,危险品摆放方式、形状差异较大等问题,导致检测难度较高。针对上述问题,本文在YOLOv4的基础上,结合空洞卷积对其网络结构进行改进,加入空洞空间金字塔池化(Atrous Space Pyramid Pooling, ASPP)模型,以此增大感受野,聚合多尺度上下文信息。然后,通过K-means聚类方法生成更适合X光安检危险品检测的初始候选框。其中,模型训练时采用余弦退火优化学习率,进一步加速模型收敛,提高模型检测精度。实验结果表明,本文提出的ASPP-YOLOv4检测算法在SIXRay数据集上的mAP达到85.23%。该方法能有效减少X光安检图像中危险品的误检率,提高小目标危险品的检测能力。
  • 图  1  ASPP-YOLOv4模型设计

    Figure  1.  ASPP-YOLOv4 model design

    图  2  YOLOv4网络结构

    Figure  2.  Network structure of YOLOv4

    图  3  结合ASPP改进的YOLOv4框架

    Figure  3.  Improved YOLOv4 framework combined with ASPP

    图  4  训练过程中的Loss下降曲线

    Figure  4.  Loss decline curves during training process

    图  5  几种方法对各类危险品的检测结果

    Figure  5.  Detection results of dangerous goods detected by different algorithms

    图  6  危险品检测效果

    Figure  6.  Detection results for dangerous goods

    表  1  锚框计算结果

    Table  1.   Calculation results of the anchor

    特征图 感受野 anchor
    13×13 (124×111)
    (171×61)
    (200×151)
    26×26 (75×34)
    (82×188)
    (93×75)
    52×52 (24×78)
    (50×67)
    (62×111)
    下载: 导出CSV

    表  2  余弦退火衰减过程

    Table  2.   Cosine annealing decay process

    算法:余弦退火衰减算法
    输入:训练epoch $E_{\rm{p} }$、训练批次${B_{\rm{s}}}$、预热期$ w\_epoch $、预先设置学习率$ \eta {}_{base} $、最大学习率$\eta _{{\rm{max}}}$、最小学习率$\eta _{{\rm{min}}}$、训练样本数$S_{\rm{c}}$;
    输出:当前训练学习率$ \eta _t^{} $
    步骤:
    (1) 初始化总步长$Step{s_{{\rm{total}}} } = \left( { {E_p} \times {S_c} } \right)/{B_s}$
    预热步长$Step{s_{{\rm{warmup}}} } = \left( {w \times {S_{\rm{c}}} } \right)/{B_{\rm{s}}}$
    (2) Repeat:
     在每次重启之后执行:
      更新当前执行的步数$step{}_{{\rm{global}}}$,并记录当前学习率
      更新学习率
      if $Steps{}_{{\rm{global}}} \lt Steps{}_{{\rm{warmup}}}$:
      根据${\eta _t} = \left( {({\eta _{ {\rm{base} } } } - {\eta _{ {\rm{warmup} } } })/Step{s_{ {\rm{warmup} } } } } \right) \times Step{s_{ {\rm{global} } } } + {\eta _{ {\rm{warmup} } } }$计算线性增长的学习率${\eta _{{\rm{warmup}}} }$
      else:
      根据${\eta _t} = \dfrac{1}{2} \times {\eta _{{\rm{base}}} } \times \cos\;\left( {1 + \left( {{\text{π}} \times \dfrac{ {(Step{s_{{\rm{global}}} } - Step{s_{{\rm{warmup}}} })} }{ {Step{s_{{\rm{total}}} } - Step{s_{{\rm{warmup}}} } } } } \right)} \right)$计算余弦退火的学习率
      ${\eta _t} = \min({\eta _t},{\eta _{\min} })$
    下载: 导出CSV

    表  3  训练超参数设计

    Table  3.   Design of the training hyperparameters

    状态 名称 参数
    冻结主干网络 batch_size 8
    epoch 50
    最大学习率 1e-3
    最小学习率 1e-6
    Warmup_epoch 10
    解冻主干网络 batch_size 2
    epoch 50
    最大学习率 1e-4
    最小学习率 1e-6
    Warmup_epoch 10
    下载: 导出CSV

    表  4  不同模型的AP比较

    Table  4.   Comparison of AP for different networks (%)

    方法 AP mAP
    Gun Knife Wrench Pliers Scissors
    YOLOv3 93.18 78.00 68.55 79.69 76.97 79.28
    M2Det 95.49 75.70 70.17 83.00 82.96 81.47
    SSD 94.91 77.87 74.82 84.51 82.69 82.96
    YOLOv4 94.40 81.69 77.38 84.50 77.55 83.11
    ASPP-YOLOv4 95.78 81.39 77.84 87.36 83.76 85.23
    下载: 导出CSV

    表  5  ASPP-YOLOv4的性能分析

    Table  5.   The performance of ASPP-YOLOv4

    类别 AP Precision Recall F1-Measure
    Gun 95.78% 98.44% 85.32% 0.91
    Knife 81.39% 91.48% 67.40% 0.78
    Wrench 77.84% 81.61% 71.05% 0.76
    Pliers 87.36% 93.15% 75.79% 0.84
    Scissors 83.76% 86.28% 76.23% 0.81
    下载: 导出CSV

    表  6  YOLOv4改进前后检测性能对比

    Table  6.   Comparison of YOLOv4 performance before and after improvement

    方法 mAP Precision Recall F1-Measure
    YOLOv4 83.11% 90.35% 73.00% 0.80
    ASPP-YOLOv4 85.23% 90.20% 75.16% 0.82
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-13
  • 修回日期:  2021-05-11
  • 网络出版日期:  2021-08-11
  • 刊出日期:  2021-11-19

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