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面结构光在机检测的叶片反光抑制技术

李茂月 刘泽隆 赵伟翔 肖桂风

李茂月, 刘泽隆, 赵伟翔, 肖桂风. 面结构光在机检测的叶片反光抑制技术[J]. 中国光学(中英文), 2022, 15(3): 464-475. doi: 10.37188/CO.2021-0194
引用本文: 李茂月, 刘泽隆, 赵伟翔, 肖桂风. 面结构光在机检测的叶片反光抑制技术[J]. 中国光学(中英文), 2022, 15(3): 464-475. doi: 10.37188/CO.2021-0194
LI Mao-yue, LIU Ze-long, ZHAO Wei-xiang, XIAO Gui-feng. Blade reflection suppression technology based on surface structured light on-machine detection[J]. Chinese Optics, 2022, 15(3): 464-475. doi: 10.37188/CO.2021-0194
Citation: LI Mao-yue, LIU Ze-long, ZHAO Wei-xiang, XIAO Gui-feng. Blade reflection suppression technology based on surface structured light on-machine detection[J]. Chinese Optics, 2022, 15(3): 464-475. doi: 10.37188/CO.2021-0194

面结构光在机检测的叶片反光抑制技术

doi: 10.37188/CO.2021-0194
基金项目: 国家自然科学基金资助项目(No. 51975169);黑龙江省普通高校基本科研业务费专项资金资助项目(No. 2019-KYYWF-0204)
详细信息
    作者简介:

    李茂月(1981—),男,山东青岛人,博士,教授,博士生导师,2004 年于南京林业大学获得学士学位,2007 年于长安大学获得硕士学位,2012 年于哈尔滨工业大学获得博士学位,主要从事智能加工与光学检测技术方面的研究。E-mail:lmy0500@163.com

    刘泽隆(1996—),男,黑龙江大庆人,硕士研究生,2019 年于哈尔滨理工大学获得学士学位,目前在哈尔滨理工大学攻读硕士学位,主要从事图像处理和机器视觉方面的研究。E-mail: LZL_LOUIS1231@163.com

  • 中图分类号: TH741

Blade reflection suppression technology based on surface structured light on-machine detection

Funds: Supported by National Natural Science Foundation of China (No. 51975169) ;the Fundamental Research Fundation for Universities of Heilongjiang Province (No. 2019-KYYWF-0204)
More Information
  • 摘要: 薄壁叶片在结构光检测过程中,由于其表面粗糙度较小,易产生强烈的反光现象,影响求解条纹相位主值,进而无法准确重构出三维点云。本文以加工过程中的叶片作为研究对象,提出一种对在机检测过程中的条纹图像进行图像增强处理的Retinex算法,恢复条纹在高反光位置的信息。首先,对薄壁叶片的反光特性进行分析,通过实验标定出最优曝光的灰度区间和理想灰度值,建立了光圈转动角度与图像平均灰度的相机响应曲线模型,调节光圈和曝光时间至最优曝光的灰度区间并以此作为检测条件。其次,基于Retinex算法处理条纹图像,通过改进的双边滤波代替常用的高斯滤波,在去除光照的同时有效保留了条纹的边缘信息。最后,对薄壁叶片进行单目结构光检测。实验结果表明,经本文算法处理后的条纹图像,通过Canny算子检测出的条纹数量最多,图像信息熵平均增长率达18.21%,解算的相位主值误差最小,利用手持式激光扫描仪检测的标准点云进行偏差分析,点云的正、负偏差分别降至0.0589 mm和−0.0590 mm,与原点云的偏差值相比分别减少了44.6%和44.1%,表面质量得到明显改善。本文提出的图像增强算法有效抑制了面结构光检测过程中的金属表面反光。

     

  • 图 1  单目结构光测量系统模型

    Figure 1.  Monocular structured light measurement system model

    图 2  薄壁叶片表面光照现象

    Figure 2.  Light phenomenon on the thin-walled blade surface

    图 3  不同曝光下图像效果。(a) 低曝光图像;(b) 高曝光图像

    Figure 3.  Image effects under different exposures. (a) Low-exposure image; (b) high-exposure image

    图 4  相机响应曲线

    Figure 4.  Camera response curve

    图 5  相机调节结构

    Figure 5.  Camera adjustment structure

    图 6  不同曝光条件下的原图像、图像灰度值及点云图像

    Figure 6.  Original images, image gray values and point cloud images under different exposure conditions

    图 7  不同环境光时的相机响应曲线

    Figure 7.  Camera response curves under different ambient lights

    图 8  相机光圈自适应调节流程图

    Figure 8.  Flow chart of camera aperture adaptive adjustment

    图 9  本文算法流程图

    Figure 9.  Flow chart of proposed algorithm

    图 10  单目结构光检测实验场景

    Figure 10.  Experimental scene of monocular structured light detection

    图 11  待测薄壁叶片

    Figure 11.  Thin-walled blade to be inspected

    图 12  经不同算法处理的条纹图像

    Figure 12.  Fringe images processed by different algorithms

    图 13  Canny算子边缘检测结果

    Figure 13.  Canny operator edge detection results

    图 14  4种算法处理前后相位主值和点云效果对比图

    Figure 14.  Comparison of phase principal values and point cloud effects before and after processing by four different algorithms

    图 15  点云偏差分析结果

    Figure 15.  Point cloud deviation analysis results

    图 16  铝合金金属板结构光检测结果

    Figure 16.  Structural light detection results of the aluminum alloy metal plate

    表  1  工业相机主要参数

    Table  1.   Main parameters of the industrial camera

    性能参数参数值
    分辨率1280(H)×1024(V)
    帧率/frame·s−130
    传感器类型CMOS
    靶面尺寸/mm7.2×5.3
    像素尺寸/μm5.2×5.2
    下载: 导出CSV

    表  2  不同方法处理前后的条纹图像信息熵

    Table  2.   Information entropies of fringe image by different processing methods

    原图SSRMSR双边本文
    频率15.69296.84786.93486.25697.0843
    频率25.95516.96117.01676.44427.2099
    频率36.74787.17587.24666.76707.3638
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-08
  • 录用日期:  2022-01-21
  • 修回日期:  2021-12-07
  • 网络出版日期:  2022-01-26
  • 刊出日期:  2022-05-20

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