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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

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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]. , 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]. , 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 1YOLO(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 672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 number TureMines FalseMines Recall Precision Map
    Before optimization 387 374 11 96.64% 97.14% 95.286%
    After optimization 387 384 4 99.22% 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
    测距仪
    测量距离/cm
    优化前算法
    测量距离/cm
    误差值/cm 误差
    1 461.3 465.9 4.6 0.99%
    2 582.0 595.3 13.3 2.28%
    3 641.5 662.4 20.9 3.26%
    4 782.6 818.0 35.4 4.52%
    5 960.5 1014.9 54.4 5.66%
    6 1083.8 1155.5 71.7 6.62%
    7 1284.8 1387.6 102.8 8.00%
    8 1343.4 1470.0 126.6 9.42%
    9 1464.5 1618.9 154.4 10.54%
    10 1584.1 1775.3 191.2 12.07%
    11 1786.5 2033.2 246.7 13.81%
    12 1844.4 2119.9 275.5 14.94%
    13 1906.7 2199.7 293.0 15.37%
    14 2088.4 2466.8 378.4 18.12%
    15 2147.9 2657.8 509.9 23.73%
    16 2285.4 2791.9 506.5 22.16%
    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 测距仪测量距离/cm 优化后算法测量距离/cm 误差值/cm 误差
    1 461.3 484.2 22.9 4.96%
    2 582.0 584.5 2.5 0.43%
    3 641.5 640.0 −1.5 −0.23%
    4 782.6 775.5 −7.1 −0.91%
    5 960.5 954.5 −6.0 −0.62%
    6 1083.8 1082.2 −1.6 −0.15%
    7 1284.8 1283.8 −1.0 −0.08%
    8 1343.4 1351.5 8.1 0.60%
    9 1464.5 1469.0 4.5 0.31%
    10 1584.1 1588.2 4.1 0.26%
    11 1786.5 1784.8 −1.7 −0.09%
    12 1844.4 1851.8 7.4 0.40%
    13 1906.7 1912.9 6.2 0.32%
    14 2088.4 2097.0 8.5 0.41%
    15 2147.9 2153.0 4.9 0.23%
    16 2285.4 2285.6 0.2 0.01%
    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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