基于最小二乘支持向量机的激光拼焊焊缝识别

Recognition of weld seam for tailored blank laser welding based on least square support vector machine

关键词:  焊缝识别|图像分割|最小二乘支持向量机|激光拼焊

Key words:  weld seam recognition|image segmentation|least square support vector machine|tailored blank laser welding

摘要:

激光拼焊焊缝质量结构光视觉检测中，对焊缝的准确识别是实现高精度检测的关键. 针对检测图像中结构光光纹畸变特征不明显，无法准确识别焊缝的问题，依据焊缝纹理特征信息，提出了一种基于最小二乘支持向量机的焊缝识别方法. 首先，分析并提取焊缝区和非焊缝区差异明显的纹理特征. 其次，训练最小二乘支持向量机模型，对焊缝进行粗识别. 最后，采用Laws纹理滤波提取焊缝区域，并通过阈值分割方法精确识别焊缝. 针对不同工艺参数下的激光拼焊焊缝开展焊缝识别试验，结果表明，该方法能够有效地识别焊缝.

Abstract:

Accurate recognition of weld seam was the key for structural-light visual inspection of weld quality with high precision in tailored blank laser welding. Because of the problem that when the distortion of laser stripe was not obvious in the image and the welding seam cannot be recognized accurately, a recognition method according to the texture information of weld seam based on least squares support vector machine was proposed in this paper. Firstly, the textural features of the image were analysed and the textural features which had obvious difference between weld seam region and non-welded region were extracted. Secondly, the least square support vector machine model was trained and the coarse recognition of weld seam was accomplished. Finally, a fine recognition was achieved by Laws texture filter and threshold segmentation. The recognition experiments were carried out for weld seam in different welding parameters and the results showed that the weld seam can be recognized effectively by this method.