摘要
在实际焊接作业中,由于强烈的光学噪声、火花飞溅及烟雾等复杂环境因素的干扰,传统依赖于单一跟踪器的焊缝检测方法常难以维持对焊缝的精确追踪,从而导致跟踪性能显著下降乃至失效。为此,提出一种基于U-net神经网络的焊缝表面缺陷红外图像实时跟踪检测方法。该方法在焊接初始阶段,通过准确识别焊缝表面红外图像中的焊缝特征,并对特征点进行精确定位。为了应对焊接过程中的强干扰环境,设计了两个并行的核相关滤波器来追踪焊缝特征点,并通过卡尔曼滤波器融合这两个跟踪器的输出结果,确保在复杂环境中也能实现焊缝的实时、稳定且鲁棒的跟踪。将实时跟踪获取的焊缝特征点信息作为关键输入送入U-net神经网络中。在U-net的架构中,引入一个分支网络,以优化特征提取过程并提升分割图的质量,增强对焊缝表面缺陷的细节捕捉能力。利用边界框机制分析U-net输出的分割图,实现对缺陷区域位置和大小的自动判定,完成焊缝表面缺陷的红外图像检测。实验结果表明,该方法在焊缝跟踪和焊缝表面缺陷红外图像检测方面均表现出色,评价函数Q值低至21.36,具有较高的检测精度。
In actual welding operations,due to the interference of complex environmental factors such as strong optical noise,spark splashing,and smoke,traditional weld seam detection methods relying on a single tracker often find it difficult to maintain accurate tracking of the weld seam,resulting in a significant decrease or even failure in tracking performance.Therefore,a real-time tracking and detection method for infrared images of weld surface defects based on Unet neural network is proposed.This method accurately identifies the weld seam features in the infrared image of the weld seam surface during the initial stage of welding,and precisely locates the feature points.In order to cope with the strong interference environment during the welding process,two parallel kernel correlation filters are designed to track the weld seam feature points,and the output results of these two trackers are fused through a Kalman filter to ensure real-time,stable,and robust tracking of the weld seam even in complex environments.Real time tracking of weld seam feature point information is used as a key input and fed into the U-net neural network.In the U-net architecture,a branch network is introduced to optimize the feature extraction process and improve the quality of the segmentation map,enhancing the ability to capture details of surface defects on the weld seam.Using the bounding box mechanism to analyze the segmentation map output by U-net,automatic determination of the position and size of defect areas is achieved,and infrared image detection of surface defects in welds is completed.The experimental results show that this method performs well in both weld seam tracking and infrared image detection of weld surface defects,with an evaluation function Q value as low as 21.36,indicating high detection accuracy.
作者
李旸
冯乃勤
孙滨
程艳艳
LI Yang;FENG Nai-qin;SUN Bin;CHENG Yan-yan(Information Engineering College,Zhengzhou University of Industrial Technology,Zhengzhou 451150,China;College of Computer and Information Engineering,Hennan Normal University,Xinxiang 453000,China;School of Applied Engineering,Henan University of Science and Technology,Sanmenxia 472000,China)
出处
《激光与红外》
北大核心
2025年第7期1142-1147,共6页
Laser & Infrared
基金
河南省科技厅科技攻关支持项目(No.242102210101)
河南省高等教育教学改革研究与实践支持项目(No.2024SJGLX0584)
河南省高等学校重点科研计划项目(No.24B120007)
河南省大中专院校就业创业课题项目(No.JYB2023094)资助。
关键词
U-net神经网络
焊缝缺陷
红外图像
实时跟踪
U-net neural network
surface defects on welds
infrared image
real time tracking
