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基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别

何银水, 孔萌, 陈华斌, 陈玉喜, 陈善本

何银水, 孔萌, 陈华斌, 陈玉喜, 陈善本. 基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别[J]. 焊接学报, 2015, 36(12): 51-55.
引用本文: 何银水, 孔萌, 陈华斌, 陈玉喜, 陈善本. 基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别[J]. 焊接学报, 2015, 36(12): 51-55.
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HE Yinshui, KONG Meng, CHEN Huabin, CHEN Yuxi, CHEN Shanben. Weld seam profile identification based on visual attention mechanism in robotic thick-plate welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(12): 51-55.
Citation: HE Yinshui, KONG Meng, CHEN Huabin, CHEN Yuxi, CHEN Shanben. Weld seam profile identification based on visual attention mechanism in robotic thick-plate welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(12): 51-55.
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基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别

  • 1.

    上海交通大学 材料科学与工程学院, 上海 200240;南昌大学 资源环境与化工学院, 南昌 330031

  • 2.

    上海发那科机器人有限公司, 上海 201906

  • 3.

    上海交通大学 材料科学与工程学院, 上海 200240

基金项目: 国家自然科学基金资助项目(61374071,51405298);上海市科委资助项目(11111100300);国家发改委资助项目(HT[2012]2144)

Weld seam profile identification based on visual attention mechanism in robotic thick-plate welding

  • 1.

    School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;School of Resources, Environmental & Chemical Engineering, Nanchang University, Nanchang 330031, China

  • 2.

    Shanghai-Fanuc Robotics Co., LTD, Shanghai 201906, China

  • 3.

    School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

摘要

    摘要
  • 摘要: 设计了一种视觉传感器同时采集熔池和焊缝轮廓, 并提出一种基于视觉注意机制的机器人厚板焊接焊缝轮廓的提取方法. 该方法以图像的亮度信息和方向信息作为初级视觉特征,并以焊缝图像作为亮度特征图,把对焊缝图像进行膨胀处理再进行Gabor滤波的结果作为方向特征图. 对上述两种特征图进行显著性度量,然后对度量结果进行自适应特征融合获取综合显著图,并对其进行阈值分割和最近邻聚类. 结果表明,最后在所有类别中以成员数最多的类所覆盖的区域作为最先被注意的区域,而其包含的数据即为焊缝轮廓信息.
    Abstract: This paper presents a novel vision sensor to capture weld pools and weld seam profiles simultaneously in the same frame for implementing autonomous route planning in robotic thick plate welding. A method of extracting the weld seam profile based on visual attention mechanism from weld pool background is proposed here. In this method, brightness and direction are selected as primary visual characteristics, and the captured image is deemed as the brightness feature map while the direction feature map is acquired by Gabor filtering. Then, the above two feature maps are measured individually by the different methods to better highlight corresponding characteristics, and the two measured feature maps are integrated into a comprehensive saliency map by a self-judging algorithm. To extract the seam profile, threshold segmentation is applied to the comprehensive saliency map and followed by nearest neighbor clustering. The maximum cluster is the extracted seam profile and considered as the first noticed region. Experimental results show the effectiveness of the proposed method.
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    • 参考文献(10)
  • [1] 邹怡蓉, 都 东, 曾锦乐, 等. 基于多视觉特征获取与信息融合的焊道识别方法[J]. 焊接学报, 2013, 34(5): 33-36. Zou Yirong, Du Dong, Zeng Jinle, et al. Visual method for weld seam recognition based on multi-feature extraction and information fusion[J]. Transactions of the China Welding Institution, 2013, 34(5): 33-36.
    [2] 张华军, 张广军, 蔡春波, 等. 机器人多层多道焊缝激光视觉焊道的识别[J]. 焊接学报, 2009, 30(4): 105-108. Zhang Huajun, Zhang Guangjun, Cai Chunbo, et al. Laser-based visual recognition of multi-pass seam in robot arc welding[J].Transactions of the China Welding Institution, 2009, 30(4): 105-108.
    [3] 蔡志勇, 陈 荣, 余伏章, 等. 小波变换的多层多道焊接拐角跟踪点的识别研究[J]. 中国图像图形学报, 2008, 13(12): 2344-2350. Cai Zhiyong, Chen Rong, Yu Fuzhang, et al. Study on multipass welding seam recognition based on wavelet transform[J]. Journal of Image and Graphics, 2008, 13(12): 2344-2350.
    [4] 王胜华, 都 东, 曾 凯, 等. 基于纹理特征的焊缝识别方法[J].焊接学报, 2008, 29(11): 5-8. Wang Shenghua, Du Dong, Zeng Kai, et al. Weld recognition based on texture feature[J]. Transactions of the China Welding Institution, 2008, 29(11): 5-8.
    [5] Itti L, Koch C, Niebur E. A model of saliency based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
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    [8] Daugman J. Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression[J]. IEEE Transactions Acoustics, Speech and Signal Processing, 1988, 36(7): 1169-1179.
    [9] Mantas M, Mancas-Thillou C, Gosselin B, et al. A rarity-based visual attention map-application to texture description[C]// IEEE International Conference on Image Processing, Atlanta, USA, 2006: 445-448.
    [10] 张巧荣. 视觉注意计算模型及其关键技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2011.
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  • 收稿日期:  2014-03-05

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