平顶光束的光斑能量集中区域轮廓提取与均匀性评价
Contour Extraction and Uniformity Evaluation on Energy Concentration Area of Flat-Top Beams
黄泽帆 1张玉莹 1董雷岗 1赵帅1
作者信息
- 1. 季华实验室光电科学与技术研究部,广东佛山 528200
- 折叠
摘要
平顶光束广泛应用于激光微加工领域,准确评价光斑能量集中区域的几何形貌与均匀性,是评判光束可用性的重要环节.获取光斑能量集中区域轮廓是分析其几何形貌的基础,平顶光束的光斑图像边缘特征不显著,常规方法难以获得包围光斑能量集中区域的轮廓边界.以激光剥离匀化整形光路中的平顶光束为例,采用自适应光斑轮廓提取算法获取光斑能量集中区域的轮廓,使用自适应灰度阈值预分割光斑图像的前景与背景,对最大连通区域进行凸包构造获得轮廓边界,基于此计算的多项几何形貌指标在包含外界噪声的测量光斑与无噪声理想光斑图像上具有一致的结果.在含明暗斑点及无规则沟纹的测量光斑与理想光斑图像上的对比结果表明,光强均方根对二者无显著数值差异,基于灰度共生矩阵计算空间灰度纹理特征,提出平顶光束的均匀性量化评价指标在测量光斑和理想光斑图像上具有显著的数值差异,有助于评估光路匀化整形效果.
Abstract
Flat-top beams are widely used in laser micromachining.Evaluation of the geometric morphology and uniformity of spot energy concentration area is important to judge the availability of beams.Obtaining the contour of energy concentration ar-ea of spot is a necessary step for analyzing geometric morphology.Routine methods are difficult to obtain the boundary sur-rounding the energy concentration area of spot as the edge features of flat-top beam are unobvious.In this paper,taking the flat-top beam in the laser homogenization system of laser lift-off equipment as an example,an adaptive contour extraction algo-rithm is proposed to obtain the contour of spot,which uses an adaptive gray threshold to divide the foreground and back-ground,and then construct a convex hull of the maximum connected area to obtain the boundary.Geometric morphology inde-xes have consistent results between measured spot with noise and ideal spot image without noise based on this algorithm.The comparison results between the measured spot images with noise and ideal spot images show that RMS of light intensity has no significant difference between them.In this paper,based on spatial gray texture features from gray co-occurrence matrix,is proposed to evaluate the uniformity of flat-top beam,which has significant difference between the measured spot and ideal spot,and is helpful to evaluate the quality of flat-top beam.
关键词
平顶光束/轮廓提取/均匀性/计算机视觉Key words
flat-top beams/contour extraction/uniformity/computer vision引用本文复制引用
基金项目
国家重点研发计划项目(2021YFB3602600)
出版年
2024
NETL
NSTL
万方数据