[1]王高伟,杨 飞,杨成林,等. 基于YOLOv11的焊缝识别跟踪算法研究[J].机械与电子,2026,44(01):65-71.
　WANG Gaowei,YANG Fei,YANG Chenglin,et al. Research on Welding Seam Identification and Tracking Algorithm Based on YOLOv11[J].Machinery & Electronics,2026,44(01):65-71.
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 基于YOLOv11的焊缝识别跟踪算法研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年01期
页码:
65-71
栏目:
智能制造
出版日期:
2026-01-27

文章信息/Info

Title:
 Research on Welding Seam Identification and Tracking Algorithm Based on YOLOv11
文章编号:
1001-2257(2026)01-0065-07
作者:
 王高伟1; 杨 飞1; 杨成林1; 刘亚东1; 胡明明2; 刘新华2
 (1.国能神东煤炭集团有限责任公司寸草塔煤矿,内蒙古 鄂尔多斯 017209;2.中国矿业大学机电工程学院,安徽 徐州 221116)
Author(s):
 WANG Gaowei1; YANG Fei1; YANG Chenglin1; LIU Yadong1; HU Mingming2; LIU Xinhua2
 (1.Cuncaota Coal Mine,Guoneng Shendong Coal Group Co.,Ltd.,Ordos 017209,China;2.School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China)
关键词:
焊缝识别; 焊缝跟踪; 特征提取; 深度学习
Keywords:
weld seam identification; weld seam tracking; feature extraction; deep learning
分类号:
TG441.7;TP391.4
文献标志码:
A
摘要:
针对自动化焊接中焊缝检测精度和实时跟踪能力不足的问题,提出了一种基于YOLOv11的焊缝识别与跟踪方法。该方法分析焊缝视觉特征,构建了适用于焊缝检测的数据集,并在YOLOv11模型的基础上结合激光条纹中心线提取算法,实现对焊接过程中焊缝的高精度检测与实时跟踪。实验结果表明,在512×512分辨率下,所提方法的识别精度达到99.51%,焊缝特征点识别的最大误差为1.05像素,推理速度为54 ms/张图片,平均定位误差小于2.2 mm,能够有效满足智能制造生产线对焊缝质量监控的严格要求。
Abstract:
To address the problems with weld seam detection accuracy and real time tracking ability in the field of automated welding,a weld seam identification and tracking method based on YOLOv11 is proposed.The method analyzes the visual features of weld seams,constructs a dataset suitable for weld seam detection,and integrates the laser stripe central line extraction algorithm with the YOLOv11 model,thereby achieving high precision detection and real time tracking of weld seams during the welding process.The experimental results show that,at a resolution of 512×512,the proposed method achieves an identification accuracy of 99.51%,with a maximum error of 1.05 pixels in weld seam feature point recognition,an inference speed of 54 ms per image,and an average location error of less than 2.2 mm.These results demonstrate that the proposed method can effectively meet the strict demands of weld seam quality monitoring in intelligent manufacturing production lines.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2025-06-23
作者简介:王高伟 (1980-),男,河北魏县人,工程师,研究方向为煤矿方面采煤和机电安全;刘亚东 (1986-),男,山西山阴人,助理工程师,研究方向为焊接自动化及焊接缺陷检测,通信作者,E-mail:liuyadong19861234@126.com。
更新日期/Last Update: 2026-03-09