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基于R-D SSD模型航空发动机安装工位检测算法

陈科山 郝宇 何泓波 李坤龙

陈科山, 郝宇, 何泓波, 等 . 基于R-D SSD模型航空发动机安装工位检测算法[J]. 北京航空航天大学学报, 2021, 47(4): 682-689. doi: 10.13700/j.bh.1001-5965.2020.0321
引用本文: 陈科山, 郝宇, 何泓波, 等 . 基于R-D SSD模型航空发动机安装工位检测算法[J]. 北京航空航天大学学报, 2021, 47(4): 682-689. doi: 10.13700/j.bh.1001-5965.2020.0321
CHEN Keshan, HAO Yu, HE Hongbo, et al. Detection algorithm of aeroengine installation station based on R-D SSD model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(4): 682-689. doi: 10.13700/j.bh.1001-5965.2020.0321(in Chinese)
Citation: CHEN Keshan, HAO Yu, HE Hongbo, et al. Detection algorithm of aeroengine installation station based on R-D SSD model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(4): 682-689. doi: 10.13700/j.bh.1001-5965.2020.0321(in Chinese)

基于R-D SSD模型航空发动机安装工位检测算法

doi: 10.13700/j.bh.1001-5965.2020.0321
基金项目: 

国家自然科学基金 51735009

详细信息
    作者简介:

    陈科山  男, 博士, 教授, 硕士生导师。主要研究方向: 机器人设计与控制技术、计算机视觉与图像识别技术、智能交通与新能源技术等

    郝宇  男, 硕士研究生。主要研究方向: 计算机视觉与图像识别技术

    通讯作者:

    陈科山. E-mail: kshchen@bjtu.edu.cn

  • 中图分类号: V239;TP181

Detection algorithm of aeroengine installation station based on R-D SSD model

Funds: 

National Natural Science Foundation of China 51735009

More Information
  • 摘要:

    为解决航空发动机在安装过程中大多实行人工安装、定位不精确等问题,在研究其自动化安装方法中,针对航空发动机安装工位的检测需求,提出了一种残差网络与膨胀卷积相融合的SSD改进算法(R-D SSD)。将经典SSD模型的主干网络VGG16替换为残差网络ResNet-101,并增加其输出特征图上的预选框数量,解决了原始算法对底层特征抓取能力不足的问题,进而弥补了对小目标检测效果较差的缺陷;利用膨胀卷积扩大网络的感受野,获取足够的安装工位边缘特征细节信息,在不改变网络结构的同时,保证了模型良好的实时性和对目标的检测精度。实验表明:对于小目标数据集和整个数据集,R-D SSD算法的平均检测精度较原始算法分别提高了8.6%和4.0%,可以满足航空发动机安装时平均检测精度不低于85%的要求。

     

  • 图 1  SSD安装工位检测效果

    Figure 1.  SSD detection effect of installation station

    图 2  R-D SSD网络结构

    Figure 2.  R-D SSD network structure

    图 3  残差学习单元

    Figure 3.  Residual learning unit

    图 4  增加预选框数量

    Figure 4.  Increasing the number of preselected boxes

    图 5  膨胀卷积

    Figure 5.  Dilation convolution

    图 6  小目标检测效果对比

    Figure 6.  Comparison of small target detection effect

    图 7  正常大小目标检测效果对比

    Figure 7.  Comparison of normal-size target detection effect

    图 8  混淆矩阵

    Figure 8.  Confusion matrix

    表  1  实验数据集划分

    Table  1.   Experimental dataset partition

    类别 训练集/张 占比/% 测试集/张 占比/%
    前安装工位 2 069 41 236 5
    后安装工位 1 881 38 192 4
    前安装工位+后安装工位 550 11 79 1
    下载: 导出CSV

    表  2  小目标检测精度对比

    Table  2.   Comparison of small target detection accuracy

    检测算法 检测对象 AP/% mAP/%
    SSD 前安装工位 67.2 71.9
    后安装工位 76.6
    R-D SSD 前安装工位 77.9 80.5
    后安装工位 83.1
    下载: 导出CSV

    表  3  正常大小目标检测精度对比

    Table  3.   Comparison of normal-size target detection accuracy

    检测算法 mAP/%
    SSD 86.7
    R-D SSD 88.1
    下载: 导出CSV

    表  4  不同检测算法性能比较

    Table  4.   Comparison of performance among different detection algorithms

    检测算法 主干网络 mAP/% 检测速度/(帧·s-1)
    Faster R-CNN ResNet50 88.5 8.2
    YOLOv3 Darknet-53 83.2 51.0
    SSD VGG16 82.9 52.6
    R-D SSD ResNet-101 86.9 40.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-06
  • 录用日期:  2020-09-04
  • 网络出版日期:  2021-04-20

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