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深度卷积神经网络在辐射环境下核废料检测中的应用

向伟 史晋芳 刘桂华 徐锋

向伟, 史晋芳, 刘桂华, 等. 深度卷积神经网络在辐射环境下核废料检测中的应用[J]. 强激光与粒子束, 2019, 31: 116001. doi: 10.11884/HPLPB201931.190220
引用本文: 向伟, 史晋芳, 刘桂华, 等. 深度卷积神经网络在辐射环境下核废料检测中的应用[J]. 强激光与粒子束, 2019, 31: 116001. doi: 10.11884/HPLPB201931.190220
Xiang Wei, Shi Jinfang, Liu Guihua, et al. Application of deep convolutional neural network in detection of nuclear waste in radiation environment[J]. High Power Laser and Particle Beams, 2019, 31: 116001. doi: 10.11884/HPLPB201931.190220
Citation: Xiang Wei, Shi Jinfang, Liu Guihua, et al. Application of deep convolutional neural network in detection of nuclear waste in radiation environment[J]. High Power Laser and Particle Beams, 2019, 31: 116001. doi: 10.11884/HPLPB201931.190220

深度卷积神经网络在辐射环境下核废料检测中的应用

doi: 10.11884/HPLPB201931.190220
基金项目: 

国防科工局核能开发科研项目 [2016]1295

国家自然科学基金项目 11602292

国家自然科学基金青年基金项目 61701421

详细信息
    作者简介:

    向伟(1994—),男,硕士研究生,从事深度学习、目标检测研究; 924717953@qq.com

    通讯作者:

    史晋芳(1977—),女,副教授,从事机器视觉工程研究; 603071939@qq.com

  • 中图分类号: X946

Application of deep convolutional neural network in detection of nuclear waste in radiation environment

  • 摘要: 针对辐射环境下核废料检测准确率低的问题,提出一种基于深度卷积神经网络的辐射环境下核废料检测算法Dense-Dilated-YOLO V3。实验结果表明,Dense-Dilated-YOLO V3在不增加参数的情况下扩大了网络感受野,也有效避免图像信息的损失,同时能够在核辐射环境下提取到更多的目标细节特征,对辐射环境下目标检测的准确率可达93.29%,比原算法提高5.53%,召回率可达91.73%,提高了8.28%,有效解决了复杂辐射环境下核废料检测准确率低的问题,为辐射环境下核废料检测提供了新的途径。
  • 图  1  Darknet-53网络结构

    Figure  1.  The structure of Darknet-53 network

    图  2  Dense-Dilated- YOLO V3结构图

    Figure  2.  The structure of Dense-Dilated-YOLO V3

    图  3  去噪预处理图像

    Figure  3.  Denoising preprocessing of image

    图  4  网络训练参数收敛折线图

    Figure  4.  Network training parameter of the convergence line graph

    图  5  YOLO V3和Dense-Dilated-YOLO V3检测效果对比图

    Figure  5.  Detection effect comparison chart of YOLO V3 and Dense-Dilated-YOLO V3

    表  1  网络性能结果对比表

    Table  1.   The comparison table of Network performance result

    evaluation index Accuracy/% RECALL/% IOU/ % MAP/ %
    YOLO V3 87.76 83.45 83.97 82.58
    Dense-Dilated-YOLO V3 93.29 91.73 88.64 90.34
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-17
  • 修回日期:  2019-09-04
  • 刊出日期:  2019-11-15

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