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抛撒地雷的夜视智能探测方法研究

王驰 于明坤 杨辰烨 李思远 李富迪 李金辉 方东 栾信群

王驰, 于明坤, 杨辰烨, 李思远, 李富迪, 李金辉, 方东, 栾信群. 抛撒地雷的夜视智能探测方法研究[J]. 中国光学, 2021, 14(5): 1202-1211. doi: 10.37188/CO.2020-0214
引用本文: 王驰, 于明坤, 杨辰烨, 李思远, 李富迪, 李金辉, 方东, 栾信群. 抛撒地雷的夜视智能探测方法研究[J]. 中国光学, 2021, 14(5): 1202-1211. doi: 10.37188/CO.2020-0214
WANG Chi, YU Ming-kun, YANG Chen-ye, LI Si-yuan, LI Fu-di, LI Jin-hui, FANG Dong, LUAN Xin-qun. Night vision intelligent detection method of scatterable landmines[J]. Chinese Optics, 2021, 14(5): 1202-1211. doi: 10.37188/CO.2020-0214
Citation: WANG Chi, YU Ming-kun, YANG Chen-ye, LI Si-yuan, LI Fu-di, LI Jin-hui, FANG Dong, LUAN Xin-qun. Night vision intelligent detection method of scatterable landmines[J]. Chinese Optics, 2021, 14(5): 1202-1211. doi: 10.37188/CO.2020-0214

抛撒地雷的夜视智能探测方法研究

doi: 10.37188/CO.2020-0214
基金项目: 国家自然科学基金(No. 41704123,No.61773249);近地面探测技术重点实验室基金( No. TCGZ2020C003)
详细信息
    作者简介:

    王 驰(1982—),男,河南太康人,博士(后),教授,2009年于天津大学获得博士学位,现为上海大学机电工程与自动化学院教师,主要从事精密测试及仪器等方面的研究。E-mail:wangchi@shu.edu.cn

    栾信群(1968—),女,江苏泰州人,硕士,高级工程师,1990年于国防科技大学获学士学位,2006年于西安交通大学获硕士学位,主要从事近地面目标探测技术研究。E-mail:xinqun_luan@126.com

  • 中图分类号: TN247; TN223; TP212.6

Night vision intelligent detection method of scatterable landmines

Funds: Supported by National Natural Science Foundation of China (No. 41704123, No. 61773249); Science and Technology on Near-Surface Detection Laboratory (No. TCGZ2020C003)
More Information
  • 摘要: 本文提出一种基于机器学习的抛撒地雷的夜视智能探测方法。首先,根据YOLO系列机器学习算法,设计并优化了抛撒地雷的智能检测网络模型;其次,根据几何光学成像的相似性原理,研究抛撒地雷的测距模型。最后,搭建抛撒地雷的夜视智能探测系统进行实验测试分析。实验结果显示,优化后抛撒地雷智能探测网络模型的准确度达到98.97%、召回率达到99.22%、均值平均精度为99.2%;在给定的实验条件下,利用优化后的抛撒地雷测距模型,对抛撒地雷的距离测算误差为±10 cm,表明利用机器学习可以用于对抛撒地雷进行智能探测。
  • 图  1  YOLO(V2)网络结构图

    Figure  1.  YOLO(V2) network structure diagram

    图  2  模型测试集的PR曲线

    Figure  2.  PR curves of the model’s test set

    图  3  距离测量原理示意图

    Figure  3.  Schematic diagram of the distance measurement principle

    图  4  实验用抛撒地雷

    Figure  4.  Scatterable landmines used in the experiment

    图  5  抛撒地雷智能探测系统图

    Figure  5.  Diagram of the intelligent detection system of scatterable landmines

    图  6  72式防坦克金属地雷

    Figure  6.  Type 72 anti-tank metal landmine

    图  7  背景为平坦地面的地雷

    Figure  7.  Scatterable landmines with flat background

    图  8  背景为草丛的58式防步兵橡胶地雷

    Figure  8.  Type 58 anti-infantry rubber landmine with grass in the background

    图  9  高斯拟合曲线图

    Figure  9.  Gaussian fitting curves

    表  1  训练参数

    Table  1.   Training parameters

    参数名称参数值
    网络权重更新的batch数目64
    网络实际训练细分批次数8
    网络训练图片的宽832
    网络训练图片的高832
    动量参数0.9
    权重衰减系数0.0005
    学习率0.001
    迭代次数100200
    下载: 导出CSV

    表  2  测试集测试时相关指标

    Table  2.   Relevant indexes during test set testing

    Instance numberTureMinesFalseMinesRecallPrecisionMap
    Before optimization3873741196.64%97.14%95.286%
    After optimization387384499.22%98.97%99.2%
    下载: 导出CSV

    表  3  抛撒地雷测距实验数据

    Table  3.   Experimental data of distance measurement for scatterable landmines

    测量
    次数
    激光测距仪
    测量距离/cm
    优化前算法
    测量距离/cm
    误差值/cm误差
    1461.3465.94.60.99%
    2582.0595.313.32.28%
    3641.5662.420.93.26%
    4782.6818.035.44.52%
    5960.51014.954.45.66%
    61083.81155.571.76.62%
    71284.81387.6102.88.00%
    81343.41470.0126.69.42%
    91464.51618.9154.410.54%
    101584.11775.3191.212.07%
    111786.52033.2246.713.81%
    121844.42119.9275.514.94%
    131906.72199.7293.015.37%
    142088.42466.8378.418.12%
    152147.92657.8509.923.73%
    162285.42791.9506.522.16%
    下载: 导出CSV

    表  4  优化算法后抛撒地雷测距实验数据

    Table  4.   Experimental data of the distance between the scatterable landmine and the camera after optimizing the algorithm

    测量次数激光测距仪测量距离/cm优化后算法测量距离/cm误差值/cm误差
    1461.3484.222.94.96%
    2582.0584.52.50.43%
    3641.5640.0−1.5−0.23%
    4782.6775.5−7.1−0.91%
    5960.5954.5−6.0−0.62%
    61083.81082.2−1.6−0.15%
    71284.81283.8−1.0−0.08%
    81343.41351.58.10.60%
    91464.51469.04.50.31%
    101584.11588.24.10.26%
    111786.51784.8−1.7−0.09%
    121844.41851.87.40.40%
    131906.71912.96.20.32%
    142088.42097.08.50.41%
    152147.92153.04.90.23%
    162285.42285.60.20.01%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-22
  • 修回日期:  2021-01-14
  • 网络出版日期:  2021-03-27
  • 刊出日期:  2021-09-18

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