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Journal of Intelligent & Robotic Systems

, Volume 81, Issue 3–4, pp 395–406 | Cite as

Autonomous Detection of Weld Seam Profiles via a Model of Saliency-Based Visual Attention for Robotic Arc Welding

  • Yinshui He
  • Yuxi Chen
  • Yanling Xu
  • Yiming Huang
  • Shanben ChenEmail author
Article

Abstract

This paper presents a method of autonomously detecting weld seam profiles from molten pool background in metal active gas (MAG) arc welding using a novel model of saliency-based visual attention. First, a vision sensor based on structured light is employed to capture laser stripes and molten pools simultaneously in the same frame. Second, to effectively detect the weld seam profile from molten pool background for next autonomous guidance of initial welding positions and seam tracking, a model of visual attention based on saliency is proposed. With respect to the enhanced effect of saliency, the proposed model is much better than the classic models in the field. According to the comprehensive saliency map created by the proposed model, the weld seam profile can be extracted after threshold segmentation and clustering are applied to it in turn. Third, different weld seam images are used to demonstrate the robustness of the proposed methodology and last, to evaluate the performance of the proposed method, a measure called profile extraction rate (PER) is computed, which shows that the extracted weld seam profile can basically meet the requirements of seam tracking and the guidance of welding torches.

Keywords

Visual attention Saliency Weld seam detection Gabor filtering Robotic welding 

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Yinshui He
    • 1
    • 2
  • Yuxi Chen
    • 1
  • Yanling Xu
    • 1
  • Yiming Huang
    • 1
  • Shanben Chen
    • 1
    Email author
  1. 1.School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Environment and Chemical EngineeringNanchang UniversityNanchangChina

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