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基于机器视觉方法的诊断设备自动瞄准技术

夏立琼 陈铭 王鹏 陈伯伦 张兴 魏惠月 杨品 李颖洁

夏立琼, 陈铭, 王鹏, 等. 基于机器视觉方法的诊断设备自动瞄准技术[J]. 强激光与粒子束, 2023, 35: 112002. doi: 10.11884/HPLPB202335.230317
引用本文: 夏立琼, 陈铭, 王鹏, 等. 基于机器视觉方法的诊断设备自动瞄准技术[J]. 强激光与粒子束, 2023, 35: 112002. doi: 10.11884/HPLPB202335.230317
Xia Liqiong, Chen Ming, Wang Peng, et al. Machine vision aided method for the autonomic diagnostic alignments[J]. High Power Laser and Particle Beams, 2023, 35: 112002. doi: 10.11884/HPLPB202335.230317
Citation: Xia Liqiong, Chen Ming, Wang Peng, et al. Machine vision aided method for the autonomic diagnostic alignments[J]. High Power Laser and Particle Beams, 2023, 35: 112002. doi: 10.11884/HPLPB202335.230317

基于机器视觉方法的诊断设备自动瞄准技术

doi: 10.11884/HPLPB202335.230317
基金项目: 国家自然科学基金项目(12075220)
详细信息
    作者简介:

    夏立琼,liqiong_xia@yeah.net

  • 中图分类号: O532+.13

Machine vision aided method for the autonomic diagnostic alignments

  • 摘要: 激光惯性约束聚变实验需要使用数十台套诊断设备从不同方位对瞬态微尺度物理过程进行诊断表征。大部分诊断设备通常需要进入巨型靶室真空环境内,在厘米到米级的不同工作距离上,对聚变靶上面毫米到数十微米的靶标进行瞄准,大部分诊断设备的瞄准精度需要达到50 μm水平。双目瞄准方法是在真空环境下实现远距离高精度瞄准的一种重要方法,但目前主要依赖人工判读图像识别靶标和手动操作诊断搭载平台运动实现对靶瞄准,特别是靶室内照明条件或诊断设备瞄准视线存在夹角等条件会严重影响靶标识别效果,对诊断设备瞄准精度造成较大影响。发展了一种基于机器视觉的诊断自动瞄准方法,采用Mask R-CNN算法并以大量模拟瞄准图进行靶标识别训练,有效解决了靶标自动判读问题,对靶标识别误差控制在8个像素点以内;同时基于实验室瞄准测试平台开展了靶标像素偏差与瞄准坐标偏离关系的离线标定,开展了算法引导下的瞄准精度测试,根据测试结果预估指向瞄准精度优于30 μm、径向瞄准精度优于50 μm,对实现诊断设备的高精度自动瞄准有一定的基础参考价值。
  • 图  1  诊断自动瞄准系统结构图

    Figure  1.  Sketch map of autonomic diagnostic alignment system

    图  2  双目视觉瞄准方法示意图及视觉图形与坐标偏移的坐标系

    Figure  2.  Sketch map of the binocular pointing alignment and the coordinate system between the vision graph and the shift in the coordinate

    图  3  目标靶点识别算法总体流程图

    Figure  3.  Flow chart of the target marker recognition

    图  4  瞄准视线夹角为105°条件下的靶标识别图

    Figure  4.  Recognition of the center of the laser entrance hole with an angle of 105° between the sightline and the hohlraum

    图  5  环境照明不佳条件下靶标识别图

    Figure  5.  Recognition of the target marker under low illumination

    图  6  瞄准验证平台

    Figure  6.  Alignment precision test in a laboratory

    图  7  $ {\alpha _1} $ 拟合曲线

    Figure  7.  Fitting of α1

    图  8  $ {\alpha _2} $ 拟合曲线

    Figure  8.  Fitting of α2

    图  9  $ \; \beta $ 拟合曲线

    Figure  9.  Fitting of β

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  • 被引次数: 0
出版历程
  • 收稿日期:  2023-09-15
  • 修回日期:  2023-10-22
  • 录用日期:  2023-10-22
  • 网络出版日期:  2023-10-26
  • 刊出日期:  2023-11-11

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