用于弧焊过程的视觉传感图像处理及特征信息提取方法

Visual sensing image processing and feature information extraction for arc welding

视觉传感是获取焊枪位置和姿态以及焊接坡口形状和尺寸等特征信息的有效手段,是实现弧焊过程闭环反馈控制和智能化焊接的基础。该文提出了用于弧焊过程的视觉传感图像处理及特征信息提取方法。研发了基于视觉与重力融合的多源传感器;为有效降低弧焊环境的强弧光及飞溅等对CCD图像的干扰,优化了相关硬件和图像预处理算法,改进并实现了基于Canny算子的边缘提取和基于迭代腐蚀的骨架细化算法。将这2种算法应用于多源传感器采集的弧焊过程中包含激光线的焊接坡口CCD图像处理,分别实现了图像中的激光线交点和焊接坡口导致的激光线弯折点坐标的有效提取和识别。对比分析了2种算法的特征信息提取速度与识别精度。结果表明,基于Canny算子的边缘提取算法完全能够满足对实时性有较高要求的弧焊过程焊缝跟踪需求。

Visual sensing is an effective means for obtaining arc-welding characteristic information,such as the position and pose of the welding torch,the shape and size of the welding groove,for closed-loop feedback control in intelligent arc welding.The paper describes a visual sensing image processing and feature extraction method for arc welding.A multi-source sensor was developed based on the fusion of visual information with the effect of gravity.The hardware and image preprocessing algorithm are optimized to reduce the interference of the strong arc light,spatter,and other effects on the CCD image.The algorithm then uses the edge extraction based on a Canny operator or the skeleton thinning algorithm based on iterative erosion.The two algorithms separately process the CCD image of the welding groove collected by the multi-source sensor to extract the laser lines,the laser line intersection coordinates and the laser line bending points coordinates caused by the welding groove.Comparison of the feature information extraction speeds and recognition accuracies of the two algorithms shows that the edge extraction algorithm based on the Canny operator can provide real-time weld seam tracking during arc welding.