基于核相关滤波器的强噪声焊缝跟踪  

Weld seam tracking method under strong noise based on kernel correlation filters

作　　者：巩书青 章国宝[1] 朱宏伟 GONG Shuqing;ZHANG Guobao;ZHU Hongwei(School of Automation,Southeast University,Nanjing 211189,China)

机构地区：[1]东南大学自动化学院,南京211189

出　　处：《激光技术》2025年第2期289-295,共7页Laser Technology

基　　金：江苏省重点研发计划资助项目(BE2020116)。

摘　　要：在基于结构光视觉的焊缝跟踪中,焊接时产生的电弧和飞溅等强噪声会大幅降低焊缝的能见度,导致跟踪失败。为了解决这一问题,提出了一种基于改进核相关滤波器的焊缝跟踪算法,以更好地适应强噪声的环境。首先获取焊缝的初始特征点,基于焊缝激光条纹的灰度分布特征,通过Steger算法提取焊缝激光条纹的中心线;接着对中心线进行滤波并求导来获取焊缝的初始特征点;最后将焊缝的初始特征点作为初始输入,应用改进的核相关滤波器对焊缝特征点进行学习与跟踪。结果表明,在强噪声干扰的情况下,该算法的平均误差为0.305 mm,最大跟踪误差为0.479 mm,取得了良好的跟踪效果并有效避免了跟踪漂移。该研究为高精度焊缝跟踪提供了有益参考。In the seam tracking based on structured light vision,strong noise such as arc and spatter generated in the welding process would greatly reduce the visibility of the weld,resulting in tracking failure.In order to solve this problem,a seam tracking algorithm based on the improved kernel correlation filter was proposed to better adapt to the strong noise environment.Firstly,the initial feature points of the weld were obtained.Based on the gray distribution characteristics of the weld laser stripe,the centerline of the weld laser stripe was extracted by Steger algorithm.Then the center line was filtered and derived to obtain the initial feature points of the weld.Finally,the initial feature points of the weld were used as the initial input,and the improved kernel correlation filter was applied to learn and track the weld feature points.The results show that,the average error of the algorithm is 0.305 mm,and the maximum tracking error is 0.479 mm in the case of strong noise interference,which achieves good tracking effect and effectively avoids tracking drift.This study provides a useful reference for high-precision seam tracking.

关 键 词：图像处理 焊缝跟踪 核相关滤波器 强噪声 结构光视觉 跟踪漂移 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TG441.7[自动化与计算机技术—计算机科学与技术]