China Mechanical Engineering

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Image Binarization Method of Equal-thickness Butt Welds Based on Regional Optimization

WANG Xingdong1;YANG Yalun1;KONG Jianyi1;SHI Yi2   

  1. 1.School of Machinery and Automation,Wuhan University of Science and Technology,Wuhan,430081
    2.Yangzhou Leader Engineering Technologies Co.,Ltd.,Yangzhou,Jiangsu,225000
  • Online:2019-07-25 Published:2019-07-30

基于区域优化的等厚对接焊缝图像二值化方法

王兴东1;杨雅伦1;孔建益1;施毅2   

  1. 1.武汉科技大学机械自动化学院,武汉,430081
    2.扬州力德工程技术有限公司,扬州,225000
  • 基金资助:
    国家自然科学基金资助项目(51874217);
    湖北省技术创新专项重大项目(2018AAA027);
    湖北省自然科学基金资助项目(2018CFC795)

Abstract: Due to the reflected surfaces of the grooves,complicated appearances of multi-layer and multi-pass welding grooves,and splatters,the images had the problems of excessive gray levels,uneven distribution of the gray level,and random noises.The image binarization method of equal-thickness butt welds was proposed based on regional optimization.An adaptive method for ROI definition was established to shrink the original images.Based on the distribution characteristics of neighborhood gray difference,an algorithm was proposed to divide the ROI images into two regions-flatness and intensity.With high-quality binary images as the target,the optimal model for the judgment of laser stripe regions was established to determine the regions with the optimal results.Binary processing was completed by using the gray mean values of the regions and was taken as threshold.The experimental results have good evaluation from subjectivity and objectivity.The method is satisfied the real-time requirements of various thickness plates and multi-layer and multi-pass welding.

Key words: equal-thickness butt weld, binarization method;region of interest(ROI), regional determination, optimization model

摘要: 坡口镜面反射、多层多道焊坡口形貌复杂和飞溅等现象,使图像存在灰度级多、灰度分布不均和随机噪声等问题,为此提出了一种基于区域优化的等厚对接焊缝图像二值化方法。提出自适应定义ROI算法以缩减图像;基于邻域灰度极差值的分布,将ROI图像分为平缓和剧烈两区域;进一步以高质量二值化图像为目标,建立了区域判定优化模型,由最优结果确定焊缝光带区域,将该区域灰度均值作为阈值,完成二值化处理。实验结果的主观和客观评价较优,满足多层多道焊和多厚度规格钢板的实时性焊接要求。

关键词: 等厚对接焊缝, 二值化方法, 感兴趣区域, 区域判定, 优化模型

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