Weld recognition based on key point detection method

GUO Zhongfeng; LIU Junchi; YANG Junlin

doi:10.12073/j.hjxb.20230204001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(1): 88-93. > DOI: 10.12073/j.hjxb.20230204001

GUO Zhongfeng, LIU Junchi, YANG Junlin. Weld recognition based on key point detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 88-93. DOI: 10.12073/j.hjxb.20230204001

Citation:

GUO Zhongfeng, LIU Junchi, YANG Junlin. Weld recognition based on key point detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 88-93. DOI: 10.12073/j.hjxb.20230204001

Citation:

GUO Zhongfeng, LIU Junchi, YANG Junlin. Weld recognition based on key point detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 88-93. DOI: 10.12073/j.hjxb.20230204001

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Weld recognition based on key point detection method

Shenyang University of Technology, Shenyang, 110870, China

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Received Date: February 03, 2023
Available Online: November 19, 2023

Graphical Abstract

Abstract

Abstract

In order to ensure the quality of automatic welding and improve the accuracy and adaptability of weld identification, a key point detection method for weld feature extraction is proposed. A weld feature extraction network was designed based on the convolutional neural network. The network extracted weld feature by convolution and pool operation. The feature map from deep layer is sampled up, and then the feature map from deep layer and shallow layer are fused to improve the accuracy of weld feature extraction. The feature point position of weld seam is predicted by the thermal image of weld seam, and the recognition and location of many kinds of groove weld seam are realized, and eliminating the need for non-maximum suppression algorithm, which improves the feature extraction speed. The network model is trained by collecting different weld feature images. The experimental results show that the root mean square error of weld feature point location is 0.187 mm. The network model designed in this research has high detection accuracy in the weld feature point recognition task, and has strong adaptability and generalization, and meets the requirements of automatic welding.
- image processing,
- deep-learning,
- weld recognition,
- feature fusion

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References (17)

References

[1]	郭吉昌, 朱志明, 于英飞, 等. 焊接领域激光结构光视觉传感技术的研究及应用[J]. 中国激光, 2017, 44(12): 2 − 6. Guo Jichang, Zhu Zhiming, Yu Yingfei, et al. Research and application of visual sensing technology based on laser structured light in welding industry[J]. Chinese Journal of Lasers, 2017, 44(12): 2 − 6.
[2]	陈新禹, 张庆新, 朱琳琳, 等. 基于激光视觉传感器的机器人实时焊缝跟踪方法[J]. 激光与红外, 2021, 51(4): 421 − 427. doi: 10.3969/j.issn.1001-5078.2021.04.004 Chen Xinyu, Zhang Qingxin, Zhu Linlin, et al. The method of real time seam tracking for robotic welding system based on laser vision sensor[J]. Laser & Infrared, 2021, 51(4): 421 − 427. doi: 10.3969/j.issn.1001-5078.2021.04.004
[3]	雷正龙, 沈健雄, 黎炳蔚, 等. 基于自动阈值的窄间隙端接焊缝识别技术[J]. 光学学报, 2018, 38(8): 4 − 6. Lei Zhenglong, Shen Jianxiong, Li Bingwei, et al. Recognition of narrow-gap edge welding seam based on autonomous threshold value[J]. Acta Optica Sinica, 2018, 38(8): 4 − 6.
[4]	余佳杰, 周建平, 薛瑞雷, 等. 基于结构光视觉和光照模型的焊缝表面质量检测[J]. 中国激光, 2022, 49(16): 2 − 4. Yu Jiajie, Zhou Jianping, Xue Ruilei, et al. Weld surface quality detection based on structured light and illumination model[J]. Chinese Journal of Lasers, 2022, 49(16): 2 − 4.
[5]	Li G, Hong Y, Gao J, et al. Welding seam trajectory recognition for automated skip welding guidance of a spatially intermittent welding seam based on laser vision sensor[J]. Sensors, 2020, 20(13): 36 − 57.
[6]	Zhang G, Zhang Y, Tuo S, et al. A novel seam tracking technique with a Four-Step method and experimental investigation of robotic welding oriented to complex welding seam[J]. Sensors, 2021, 21(9): 30 − 67.
[7]	Tian Y Z, Liu H F, Li L, et al. Robust identification of weld seam based on region of interest operation[J]. Advances in Manufacturing, 2020, 8(4): 473 − 485. doi: 10.1007/s40436-020-00325-y
[8]	朱齐丹, 王彦柯, 朱伟, 等. 基于结构光的焊点智能识别算法设计[J]. 焊接学报, 2019, 40(7): 82 − 87. doi: 10.12073/j.hjxb.2019400186 Zhu Qidan, Wang Yanke, Zhu Wei, et al. Intelligent recognition algorithm of welding point based on structured light[J]. Transactions of the China Welding Institution, 2019, 40(7): 82 − 87. doi: 10.12073/j.hjxb.2019400186
[9]	陈凯, 王海. 基于深度学习的焊缝图像识别研究[J]. 安徽工程大学学报, 2022, 37(1): 24 − 31. Chen Kai, Wang Hai. Research on weld Image recognition based on deep learning[J]. Journal of Anhui Polytechnic University, 2022, 37(1): 24 − 31.
[10]	杨国威, 周楠, 杨敏, 等. 融合卷积神经网络和相关滤波的焊缝自动跟踪[J]. 中国激光, 2021, 48(22): 3 − 6. Yang Guowei, Zhou Nan, Yang Min, et al. Automatic weld tracking based on convolution neural network and correlation filter[J]. Chinese Journal of Lasers, 2021, 48(22): 3 − 6.
[11]	唐溪, 姚锡凡, 董艺, 等. 基于改进CenterNet的焊缝起始向量检测与机器人位姿估计方法[J]. 计算机集成制造系统, 2022, 28(9): 2865 − 2880. Tang Xi, Yao Xifan, Dong Yi, et al. Weld initial vector detection and robot pose estimation based on improved centerNet[J]. Computer Integrated Manufacturing Systems, 2022, 28(9): 2865 − 2880.
[12]	郭鹏飞. 基于深度学习的焊缝识别及路径生成系统研究[D]. 哈尔滨: 哈尔滨工业大学, 2020. Guo Pengfei. Research on welding seam detection and path generation system based on deep learning[D]. Harbin: Harbin Institute of Technology, 2020.
[13]	刘欢, 吴亮红, 张侣, 等. 基于特征双融合CenterNet的白细胞检测方法[J]. 计算机应用, 2023, 43(8): 2602 − 2610. Liu Huan, Wu Lianghong, Zhang Lü, et al. Leukocyte detection method based on twica-fusion-feature CenterNet[J]. Journal of Computer Applications, 2023, 43(8): 2602 − 2610.
[14]	Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 5693 − 5703.
[15]	Hei Law, Jia Deng. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 2020, 128(3): 642 − 656. doi: 10.1007/s11263-019-01204-1
[16]	倪涛, 张泮虹, 李文航, 等. 基于关键点预测的装配机器人工件视觉定位技术[J]. 农业机械学报, 2022, 53(6): 443 − 450. Ni Tao, Zhang Panhong, Li Wenhang, et al. Research on visual positioning technology of assembly robot workpiece based on prediction of key points[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(6): 443 − 450.
[17]	李昱. 基于结构光的焊接机器人焊缝图像识别算法研究[D]. 长春: 长春工业大学, 2022. Li Yu. Research on image recognition algorithm for welding seam of welding robot based on structured light[D]. Changchun: Changchun University of Technology, 2022.

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Weld recognition based on key point detection method