摘要
针对复杂物体动态三维测量中条纹图像过曝光、欠曝光以及环境光照干扰引起激光中心线提取速度慢、提取不准确的问题,提出一种基于深度学习语义分割技术的光条中心线提取方法,该方法使用改进的UNet++模型进行图像分割,粗提光条中心区域,得到1~2个像素宽度的光条中心线,再利用灰度重心法精确提取亚像素中心。经实验证明,该方法能够有效克服因光条图像曝光不均以及外部干扰噪声带来的不良影响,准确、快速地提取出了复杂物体完整、光滑的亚像素光条中心线,满足工业中动态三维测量的要求。
Aiming at the problem of multiple extraction or incomplete extraction of the center position of the light strip caused by overexposure and underexposure of the fringe image in the three-dimensional measurement of complex objects,a light strip center line extraction method based on deep learning semantic segmentation technology is proposed.The method uses the improved UNet++network for semantic segmentation,roughly extracts the center area of the light strip,and obtains the light strip center line with one to two pixel width,Then the sub-pixel center is accurately extracted by the gray center of gravity method.Experiments show that this method can effectively overcome the adverse effects caused by uneven exposure of light bar images,and accurately extract the complete and smooth sub-pixel light bar centerline of complex surface objects.
作者
张宇
黄丹平
田颖
李滨
Zhang Yu;Huang Danping;Tian Ying;Li Bin(School of Mechanical Engineering,Sichuan University of Science and Engineering,Yibin 644000,China)
出处
《电子测量技术》
北大核心
2023年第1期167-172,共6页
Electronic Measurement Technology
基金
过程装备与控制工程四川省高校重点实验室开放基金科研项目(GK202209)
自贡市科技局重点项目(2019YYJC12)资助
关键词
线结构光
三维测量
复杂物体
中心提取
语义分割
line structured light
three-dimensional measurement
complex object
center extraction
semantic segmentation
