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基于改进SSD的工件定位算法

李琳 符明恒 张铁 邹焱飚

李琳,符明恒,张铁,等. 基于改进SSD的工件定位算法[J]. 北京航空航天大学学报,2023,49(6):1260-1269 doi: 10.13700/j.bh.1001-5965.2021.0442
引用本文: 李琳,符明恒,张铁,等. 基于改进SSD的工件定位算法[J]. 北京航空航天大学学报,2023,49(6):1260-1269 doi: 10.13700/j.bh.1001-5965.2021.0442
LI L,FU M H,ZHANG T,et al. A workpiece location algorithm based on improved SSD[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1260-1269 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0442
Citation: LI L,FU M H,ZHANG T,et al. A workpiece location algorithm based on improved SSD[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1260-1269 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0442

基于改进SSD的工件定位算法

doi: 10.13700/j.bh.1001-5965.2021.0442
基金项目: 广东省科技计划(2020A0103010)
详细信息
    通讯作者:

    E-mail:merobot@scut.edu.cn

  • 中图分类号: TP391.7;TP249

A workpiece location algorithm based on improved SSD

Funds: Science and Technology Program of Guangdong Province (2020A0103010)
More Information
  • 摘要:

    工业机器人完成工件的拾取、分拣与装配等任务,需要获得准确的位置信息。而目标检测算法的回归损失函数的设定会直接影响预测框的定位准确性。针对SSD原始回归损失函数忽略4个边界信息的相关性及与评价指标IoU变化不匹配等问题,提出了一种基于改进SSD的工件定位算法。所提算法以高效交并比(EIoU)为SSD的回归损失函数,将4个边界信息作为一个整体,并添加了中心点损失和边长损失2个惩罚项分别表征预测框与真实框的中心点相对距离和边长差异,解决了边框回归不准确的问题。实验结果表明:所提算法能把定位平均误差控制在0.18 mm以内,误差峰值控制在0.76 mm以内。所提算法能有效提高工件的定位精度,适用于不同类型的工件或其他类似的定位任务,具有良好的工业应用前景。

     

  • 图 1  边框回归的IoU损失

    Figure 1.  IoU loss for bounding box regression

    图 2  预测框与真实框不相交的比较

    Figure 2.  Comparison of non-intersect between prediction box and ground truth box

    图 3  边框回归的GIoU损失

    Figure 3.  GIoU loss for bounding box regression

    图 4  不同的相对位置比较

    Figure 4.  Comparison of different relative positions

    图 5  不同的预测框形状比较

    Figure 5.  Comparison of different shapes of prediction boxes

    图 6  边框回归的CIoU损失

    Figure 6.  CIoU loss for bounding box regression

    图 7  边框回归的EIoU损失

    Figure 7.  EIoU loss for bounding box regression

    图 8  不同回归损失函数的比较

    Figure 8.  Comparison of different regression loss functions

    图 9  SSD网络结构

    Figure 9.  Network structure of SSD

    图 10  边框回归

    Figure 10.  Regression of bounding box

    图 11  不同类型的工件

    Figure 11.  Different kinds of workpieces

    图 12  标注流程

    Figure 12.  Flow chart of annotation

    图 13  定位误差

    Figure 13.  Location error

    图 14  不同类型工件的定位误差

    Figure 14.  Location errors of different kinds of workpieces

    图 15  工件定位实验平台及各部分之间的联系

    Figure 15.  Experimental platform of workpiece location and relationship between various parts

    图 16  工件定位流程

    Figure 16.  Flowchart of workpiece location

    图 17  工件中心点偏差预测

    Figure 17.  Prediction of workpiece center point offset

    图 18  不同损失函数的定位平均误差

    Figure 18.  Average location error of different regression loss functions

    表  1  不同类型回归损失函数工件定位性能的比较

    Table  1.   Comparisons of performance of different regression loss functions on workpiece location

    回归损失函数平均误差/mm最大误差/mm推理速度/fps
    Smooth L10.291.1917.74
    GIoU0.241.4514.12
    CIoU0.210.9814.51
    EIoU0.180.7614.31
     注:fps表示帧/s。
    下载: 导出CSV

    表  2  本文算法与原始SSD算法的定位误差比较

    Table  2.   Comparison of location errors with original SSD algorithm %

    类型最大误差相对降低比例平均误差相对降低比例
    GIoUCIoUEIoUGIoUCIoUEIoU
    类型-Ⅰ403843615861
    类型-Ⅱ202436253134
    类型-Ⅲ−5252601832
    类型-Ⅳ−67−1321−30−713
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-04
  • 录用日期:  2022-04-10
  • 网络出版日期:  2022-05-09
  • 整期出版日期:  2023-06-30

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