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八度卷积和双向门控循环单元结合的X光安检图像分类

吴海滨,魏喜盈,王爱丽,岩堀祐之

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吴海滨, 魏喜盈, 王爱丽, 岩堀祐之. 八度卷积和双向门控循环单元结合的X光安检图像分类[J]. , 2020, 13(5): 1138-1146. doi: 10.37188/CO.2020-0073
引用本文: 吴海滨, 魏喜盈, 王爱丽, 岩堀祐之. 八度卷积和双向门控循环单元结合的X光安检图像分类[J]. , 2020, 13(5): 1138-1146.doi:10.37188/CO.2020-0073
WU Hai-bin, WEI Xi-ying, WANG Ai-li, YUJI Iwahori. X-ray security inspection images classification combined octave convolution and bidirectional GRU[J]. Chinese Optics, 2020, 13(5): 1138-1146. doi: 10.37188/CO.2020-0073
Citation: WU Hai-bin, WEI Xi-ying, WANG Ai-li, YUJI Iwahori. X-ray security inspection images classification combined octave convolution and bidirectional GRU[J].Chinese Optics, 2020, 13(5): 1138-1146.doi:10.37188/CO.2020-0073

八度卷积和双向门控循环单元结合的X光安检图像分类

doi:10.37188/CO.2020-0073
基金项目:国家自然科学基金(No. 61671190)
详细信息
    作者简介:

    吴海滨(1977—),男,上海人,博士,教授,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、深度学习图像分类方面的研究。E-mail:woo@hrbust.edu.cn

    王爱丽(1979—),女,天津人,博士,副教授,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、深度学习图像分类方面的研究。E-mail:aili925@hrbust.edu.cn

  • 中图分类号:TP391.4

X-ray security inspection images classification combined octave convolution and bidirectional GRU

Funds:Supported by National Natural Science Foundation of China (No. 61671190)
More Information
  • 摘要:针对主动视觉安检方法准确率低、速度慢,不适用于实时交通安检的问题,提出了八度卷积(OctConv)和注意力机制双向门控循环单元(GRU)神经网络相结合的X光安检图像分类方法。首先,利用八度卷积代替传统卷积,对输入的特征向量进行高低分频,并降低低频特征的分辨率,在有效提取X光安检图像特征的同时,减少了空间冗余。其次,通过注意力机制双向GRU,动态学习调整特征权重,提高危险品分类准确率。最后,在通用SIXRay数据集上的实验表明,对8000幅测试样本的整体分类准确率(ACC)、特征曲线下方面积(AUC)、正类分类准确率(PRE)分别为98.73%、91.39%、85.44%,检测时间为36.80 s。相对于目前主流模型,本文方法有效提高了X光安检图像危险品分类的准确率和速度。

  • 图 1X光安检图像分类算法框图

    Figure 1. Block diagram of X-ray security image classification algorithm

    图 2八度卷积结构

    Figure 2.The structure of octave convolution

    图 3双层BiGRU结构

    Figure 3.The structure of double-layer BiGRU

    图 4SIXray 数据集

    Figure 4.SIXRay dataset

    表 1SIXray数据集样本分布

    Table 1.Sample distribution in SIXray dataset

    正类样本 (8929) 负类样本
    枪支 刀具 扳手 钳子 剪子
    3131 1943 2199 3961 983 1050302
    下载: 导出CSV

    表 2不同类别数据增强前后对比结果

    Table 2.Comparison results of different types of data before and after data augmentation

    种类 增强前后 负类样本数 正类样本数 不平衡比率
    枪支 增强前 72255 2705 26.27
    增强后 89672 12659 7.08
    刀具 增强前 73212 1748 41.88
    增强后 93723 8608 10.89
    扳手 增强前 72948 2012 36.26
    增强后 92380 9951 9.28
    钳子 增强前 71524 3436 20.82
    增强后 85574 16757 5.10
    剪子 增强前 74153 807 91.89
    增强后 99760 2571 38.80
    下载: 导出CSV

    表 3不同模型的ACC (%)比较

    Table 3.Comparison of ACC (%) for different network modules

    方法 枪支 刀具 扳手 钳子 剪子 平均
    InceptionV3 94.63 87.52 88.97 80.50 96.95 89.71
    VGG19 97.88 98.36 97.48 96.03 97.33 97.42
    ResNet 98.36 99.20 98.16 96.10 97.80 97.92
    DenseNet 98.69 99.25 98.18 96.16 97.65 97.99
    STN-DenseNet 99.15 98.73 97.52 96.32 98.46 98.03
    OnlyBiGRU 98.77 99.40 97.73 94.37 99.14 97.88
    CNN-ABiGRU 98.89 99.42 98.89 97.07 98.96 98.65
    OctConv-ABiGRU 98.60 99.25 99.10 97.50 99.20 98.73
    下载: 导出CSV

    表 4不同模型的AUC (%) 比较

    Table 4.Comparison of AUC (%) for different network modules

    方法 枪支 刀具 扳手 钳子 剪子 平均
    InceptionV3 63.34 54.57 51.33 52.92 50.74 54.57
    VGG19 93.34 89.03 77.49 76.57 71.08 81.50
    ResNet 94.06 88.68 76.00 73.92 60.45 78.64
    DenseNet 93.91 90.37 72.59 74.65 61.08 78.52
    STN-DenseNet 95.69 93.58 75.60 76.98 65.09 81.39
    OnlyBiGRU 92.73 93.90 68.03 73.33 89.42 83.48
    CNN-ABiGRU 93.96 93.94 82.22 80.09 87.99 87.65
    OctConv-ABiGRU 91.53 94.59 87.84 86.15 96.70 91.39
    下载: 导出CSV

    表 5不同网络用时比较

    Table 5.Comparison of detection time for different network modules

    方法 参数量(百万) 模型大小(MB) 检测时间(s)
    VGG19 45.12 344 41.56
    DenseNet 57.22 437 24.91
    CNN-ABiGRU 14.42 108 75.14
    OctConv-ABiGRU 121.47 1382 36.80
    下载: 导出CSV

    表 6不同方法的分类精度比较

    Table 6.Comparison of PRE (%) for different network modules

    方法 枪支 刀具 扳手 钳子 剪子 平均
    VGG19 87.20 86.40 56.60 55.20 46.20 66.32
    DenseNet 88.20 82.18 51.25 54.50 38.50 62.93
    CNN-ABiGRU 88.50 87.20 63.00 61.20 76.40 75.26
    OctConv-ABiGRU 86.78 92.22 77.44 76.22 94.56 85.44
    下载: 导出CSV
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  • 收稿日期:2020-04-23
  • 修回日期:2020-06-15
  • 网络出版日期:2020-09-16
  • 刊出日期:2020-10-01

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