A Seam Tracking Method Based on an Image Segmentation Deep Convolutional Neural Network

被引:10
作者
Lu, Jun [1 ]
Yang, Aodong [1 ]
Chen, Xiaoyu [1 ]
Xu, Xingwang [1 ]
Lv, Ri [1 ]
Zhao, Zhuang [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Elect Engn & Optoelect Technol, Nanjing 210094, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
seam track; PAW; passive vision; image segmentation; convolutional neural network;
D O I
10.3390/met12081365
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Vision-based welding seam tracking is an important and unique branch of welding automation. Active vision seam tracking systems achieve accurate feature extraction by using an auxiliary light source, but this will introduce extra costs and the real-time performance will be affected. In contrast, passive vision systems achieve better real-time performance and their structure is relatively simple. This paper proposes a passive vision welding seam tracking system in Plasma Arc Welding (PAW) based on semantic segmentation. The BiseNetV2 network is adopted in this paper and online hard example mining (OHEM) is used to improve the segmentation effect. This network structure is a lightweight structure allowing effective image feature extraction. According to the segmentation results, the offset between the welding seam and the welding torch can be calculated. The results of the experiments show that the proposed method can achieve 57 FPS and the average error of the offset calculation is within 0.07 mm, meaning it can be used for real-time seam tracking.
引用
收藏
页数:14
相关论文
共 20 条
[1]   Narrow gap deviation detection in Keyhole TIG welding using image processing method based on Mask-RCNN model [J].
Chen, Yunke ;
Shi, Yonghua ;
Cui, Yanxin ;
Chen, Xiyin .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 112 (7-8) :2015-2025
[2]   A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes [J].
Ge, J ;
Zhu, Z ;
He, D ;
Chen, L .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2005, 26 (9-10) :1006-1011
[3]   TRACKING CONTROL-SYSTEM USING IMAGE SENSOR FOR ARC-WELDING [J].
KAWAHARA, M .
AUTOMATICA, 1983, 19 (04) :357-363
[4]  
KIM JS, 1995, PROCEEDINGS OF THE 1995 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL, P363, DOI 10.1109/ISIC.1995.525084
[5]  
Li X, 2018, 32 C NEURAL INFORM P
[6]   A robust butt welding seam finding technique for intelligent robotic welding system using active laser vision [J].
Muhammad, Jawad ;
Altun, Halis ;
Abo-Serie, Essam .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (1-4) :13-29
[7]   A robust weld seam detection method based on particle filter for laser welding by using a passive vision sensor [J].
Shao, WenJun ;
Liu, XiuFeng ;
Wu, ZiJun .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 104 (5-8) :2971-2980
[8]   Training Region-based Object Detectors with Online Hard Example Mining [J].
Shrivastava, Abhinav ;
Gupta, Abhinav ;
Girshick, Ross .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :761-769
[9]   Quality assessment in laser welding: a critical review [J].
Stavridis, John ;
Papacharalampopoulos, Alexios ;
Stavropoulos, Panagiotis .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (5-8) :1825-1847
[10]  
Wei S.B., 2011, Robotic Welding Intelligence Automation, V88, P41, DOI [10.1007/978-3-642-19959-25, DOI 10.1007/978-3-642-19959-25]