摘要
针对浑浊水体中光条纹中心点提取精度低、抗干扰能力弱的问题,提出一种改进的内部推进算法,旨在提升复杂环境下光条纹中心点提取的准确性和鲁棒性。首先利用中值滤波预处理图像以抑制噪声,结合八邻域法快速定位光条纹起始点;随后引入灰度邻域属性法,动态估算当前行的光条纹像素宽度,并基于此范围应用最大类间方差法自适应确定二值化阈值,有效减少背景干扰;最后在约束的像素宽度范围内采用灰度重心法计算初始中心点,并以此为基础向上、下方向推进搜索光条纹中心点。实验在多种浑浊水体环境及不同结构光形态下进行对比测试。结果表明,与原始内部推进算法相比,该方法均方根误差降低了13.33%,算法运行速度较Steger算法提升了69.07%,实现了精度与速度的平衡。
Aiming at the problems of low extraction accuracy and weak anti-interference ability of line structure light centroids in turbid water bodies,this study proposes an improved internal advancement algorithm,which aims to enhance the accuracy and robustness of the extraction of line structure light centroids in complex environments.Firstly,the median filter is used to preprocess the image to suppress the noise,and combined with the eight-neighborhood method to quickly locate the starting point of the light stripe;subsequently,the grayscale neighborhood attribute method is introduced to dynamically estimate the pixel width of the current line of the line structured light,and based on this range,the maximum interclass variance method is applied to adaptively determine the binarized threshold value,which effectively reduces the background interference;finally,the grayscale gravity method is used to calculate the initial centroid in the constrained range of pixel widths and use this as the basis to advance upward and downward to search for the center point of the line structured light.Comparison tests are conducted in various turbid water environments and different structured light patterns.The results show that compared with the original internal advancement algorithm,the root mean square error of the improved algorithm is reduced by 13.33%,and the running speed of the algorithm is increased by 69.07%compared with Steger’s algorithm,thus realizes the balance between accuracy and speed.
作者
许丽
冀亚
周珍
曾丹
Xu Li;Ji Ya;Zhou Zhen;Zeng Dan(School of Information Engineering,North China University of Water and Electric Power,Zhengzhou 450046,China;Henan Province Natural Resources Comprehensive Security Center,Department of Natural Resources of Henan Province,Zhengzhou 450016,China;School of Communication&Information Engineering,Shanghai University,Shanghai 200444,China)
出处
《强激光与粒子束》
北大核心
2025年第6期35-42,共8页
High Power Laser and Particle Beams
基金
河南省研究生教育教改项目和自科项目(YJS2024AL001)。
关键词
线激光
中心点提取
自适应
像素宽度
structured light
center point extraction
self-adaption
pixel width
