), 李岱泽, 赵梓宇, 钱韦旭 南昌大学 资源环境与化工学院, 南昌 330031
收稿日期:2020-03-10出版日期:2021-06-28发布日期:2021-06-30作者简介:何银水(1979-),男,安徽省安庆市人,讲师,主要从事机器人焊缝图像识别及其控制方面的研究工作.电话(Tel.):13767050849;E-mail: 基金资助:国家自然科学基金(62066029);国家自然科学基金(51665037);南昌航空大学无损检测技术教育部重点实验室开放基金(EW201980090)Weld Seam Profile Identification with T-Joints Based on Intensity Mutation and Density Feature Detection for Thick Plate Welding Process
HE Yinshui(), LI Daize, ZHAO Ziyu, QIAN Weixu School of Resources Environmental and Chemical Engineering, Nanchang University, Nanchang 330031, China
Received:2020-03-10Online:2021-06-28Published:2021-06-30摘要/Abstract
摘要： 基于激光视觉传感的厚板电弧焊接中,为实现自动化、智能化,焊接需要有效的焊缝轮廓提取方法.针对厚板T形接头熔化极活性气体保护焊中焊缝图像强烈的电弧干扰背景,提出了一种基于亮度突变性与密度特征检测的焊缝轮廓识别方法.首先,采用改进的Canny算法凸显焊缝轮廓边缘并抑制电弧干扰背景.然后,利用激光条纹在局部区域内存在亮度突变的特性,提出一种亮度突变检测方法对干扰背景进一步抑制.最后,在Otsu阈值分割处理的基础上,提出了基于带宽与密度特征逐列扫描去除干扰的算法,并将处理后的数据进行最近邻聚类,利用数据类的空间尺度特征识别焊缝轮廓.结果表明:提出的算法在处理电弧区域占图像面积近20%的强干扰时,能有效地提取不同填充阶段焊缝轮廓95%以上的整体信息,可为不同接头机器人自动化、智能化焊接的进一步实施提供参考.
关键词: 亮度突变性, 密度特征检测, 改进Canny算法, 焊缝轮廓识别, 视觉传感, T形接头
Abstract: There is a need for the effective weld seam profile extraction method to realize the automatic and intelligent welding process with thick steel plates based on laser vision sensing. In this paper, a method was proposed to identify the variable weld seam profiles from the strong arc background based on intensity mutation and density feature detection for the thick plate welding process with T-joints. First, an improved Canny algorithm was used to magnify the weld seam profile and restrain interference. Next, an intensity mutation detection method was proposed to further strengthen the weld seam profile because there exists intensity mutation in the local region surrounding the weld seam profile. Finally, an algorithm based on the band-width and density feature scanning method was proposed to further eliminate the interference data after the strengthened image was binarized with the Otsu algorithm. The weld seam profile was identified as clusters with their spatial scale features after the nearest neighbor clustering was dealt with. The results show that this method can identify over 95% of the weld seam profiles from the arc interference background whose area is about 20% of the image. It provides valuable reference for promoting the automatic and intelligent welding process with different joints.
Key words: intensity mutation, density feature detection, improved Canny algorithm, weld seam profile identification, visual sensing, T-joints
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