Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding
Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding
复制标题
用于机器人气体保护焊中基于视觉的焊缝跟踪的强噪声图像处理
DOI：
10.1007/s00170-018-3115-2
复制
发表时间：
2019-04-01
影响因子：
3.4
通讯作者：
Chen, Shanben
中科院分区：
文献类型：
--
作者：
Du, Rongqiang;Xu, Yanling;Chen, Shanben
在基于视觉传感器的机器人焊缝跟踪中，图像处理算法的鲁棒性至关重要，它将直接影响焊缝成形质量的准确性。特别是在气体保护金属极电弧焊（GMAW）焊接过程中，图像中存在大量的强噪声.研究了机器人GMAW焊接过程中存在的几种噪声较强的焊缝图像，如非典型焊缝、强弧光、大飞溅等的图像处理算法。该算法基于专用视觉传感系统，采用快速图像分割、卷积神经网络（CNN）特征区域识别和特征搜索技术，准确识别焊缝特征。该算法将阈值的选取范围从0.5 × 10（7）增加到0.9 × 10（7），降低了参数调整的难度，提高了焊缝跟踪系统的稳定性。CNN模型对非典型焊缝的识别准确率为98.0%。为了评估该算法的鲁棒性，使用两个典型的强噪声图像的实验验证的准确性。实验表明，特征提取精度的平均误差分别为0.26mm和0.29mm。结果表明，该算法能够准确有效地提取强噪声背景下的焊缝图像特征。
The robustness of the image processing algorithm is very important based on vision sensor in robotic seam tracking, which will directly affect the accuracy of weld seam shaping quality. Especially in GMAW (Gas Metal Arc Welding), there is a lot of strong noise image. This paper studies an algorithm for the several weld seam images with strong noise in robotic GMAW, such as the atypical weld seam, the strong arc light and the large spatter. Based on a purpose-built visual sensing system, the fast image segmentation, the feature area recognition of the convolutional neural network (CNN), and the feature search technique are used to identify the weld seam features accurately in the algorithm. The selection range of the threshold is increased from 0.5x10(7) to 0.9x10(7) by using the proposed algorithm, which reduces the difficulty of parameter adjustment and increases the stability of seam tracking system. And, the accuracy of the CNN model was 98.0% for the atypical weld seam identification. To evaluate the robustness of the proposed algorithm, the accuracy is verified using experiments on two typical strong noise images. The experiments show that the average error of feature extraction accuracy is 0.26mm and 0.29mm. The results show that the proposed algorithm can extract the feature of weld seam image with strong noise accurately and effectively.