SIA OpenIR  > 光电信息技术研究室
面向焊缝跟踪的线结构光图像处理
Alternative TitleLine Structured Light Image Processing for Seam Tracking
张世宽
Department光电信息技术研究室
Thesis Advisor吴清潇
Keyword焊缝跟踪 结构光条纹 深度学习 中心线提取 焊缝特征点
Pages67页
Degree Discipline模式识别与智能系统
Degree Name硕士
2021-05-21
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract为了更好地推动工业制造业的持续发展,焊接技术需要不断进行革新,从而提升焊接效率及焊接质量。基于结构光视觉传感器的焊缝自动跟踪系统可以对焊缝图像进行实时检测并提取焊缝特征点,从而控制焊接路径,实现智能焊接,因此该系统能够更好地满足现代化焊接需求。但是焊接过程中的噪声严重地影响了焊缝图像处理过程,不利于结构光条纹检测以及焊缝特征点的提取。本文针对焊缝跟踪过程中的线结构光图像处理技术进行了深入研究,从而在复杂噪声环境下对焊缝图像中的结构光条纹以及焊缝特征点进行精准检测,主要内容包括:首先,为了在包含烟尘、飞溅线、弧光等噪声的焊缝图像中精确地提取结构光条纹,提出了利用语义分割与目标检测相结合的深度学习模型来检测焊缝图像的方法。为了提高模型的检测速度,在语义分割分支中,通过添加并行下采样模块及缩减卷积核数量的策略对模型进行了优化,并使该分支与目标检测分支的特征提取部分共享权重。针对焊缝图像中结构光条纹与背景像素比例失衡而导致模型分割结果偏向负样本的问题,在损失函数中添加Dice系数来对模型进行修正。经实验验证,该方法在保证实时性的基础上,以较高的精度实现了结构光条纹的检测。其次,在得到焊缝结构光条纹的基础上,分别利用中线法、质心法以及细化法的原理来提取激光条纹的中心线,并从算法的提取精度、鲁棒性以及实时性等角度进行对比,最终选择了利用质心法的原理来提取激光条纹中心线。之后通过Hough变换直线检测并求交点的方法来提取焊缝特征点。最后,利用PyQt/C++/Opencv等编程语言以及深度学习模型框架设计了焊缝图像处理软件,将上述算法进行集成,并在实际焊接场景中进行测试。测试结果表明,所提的焊缝图像处理算法实时性较好,在复杂噪声环境下得到的检测结果质量较高,能够精确提取焊缝特征点,具有实际应用意义。
Other AbstractIn order to better promote the continuous development of the industrial manufacturing, welding technology needs to be continuously innovated to improve efficiency and quality. The automatic welding seam tracking system based on structured light vision sensor can detect the welding seam image and extract the welding seam feature points in real time, thereby controlling the welding path and achieving intelligent welding. Therefore, the welding seam tracking system can better meet the needs of modern welding. However, the noise in the welding process seriously affects the process of weld image processing, which is not conducive to structured light stripes detection and weld feature points extraction. This thesis focuses on the line structured light image processing technology in the welding seam tracking process, so as to accurately detect the structured light stripes and weld feature points in the weld image under a complex noise environment. The main contents are as follows. Firstly, in order to accurately extract structured light stripes from weld images that contain noise such as smoke, spatter lines and arcs, a deep learning model combining semantic segmentation and object detection was proposed to detect the weld images. In the semantic segmentation branch, the model was optimized by adding parallel down-sampling modules and reducing the number of convolution kernels to increase the detection speed, and the feature extraction parts of this branch and the object detection branch shared the weights. Aiming at the problem that the proportion unbalance of structured light stripes and background pixels in the weld images caused the model segmentation results to be biased towards negative samples, we introduced a Dice coefficient into the loss function to correct the model. The experimental results show that the proposed method can achieve the extraction of structured light stripes with high accuracy on the basis of ensuring real-time performance. Secondly, on the basis of obtaining the structured light stripes, the center line of the laser stripes was extracted based on the principle of the center line method, the centroid method and the thinning method. After comparing the extraction accuracy, robustness, and real-time performance of the algorithms, the centroid method was chosen to extract the center line of the laser stripes. Then, the hough transform method was used to detect the straight line equation of the center line and find the intersection point, which was regarded as the welding seam feature point. Finally, using programming languages such as PyQt/C++/Opencv and deep learning model framework to design weld image processing software, integrate the above algorithms, and test them in actual welding scenarios. The experimental results show that the proposed welding seam image processing algorithm has better real-time performance. In addition, the proposed algorithm can obtain excellent detection results and accurately extract the welding seam feature points in a complex noise environment, so it has practical application significance.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/28941
Collection光电信息技术研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
张世宽. 面向焊缝跟踪的线结构光图像处理[D]. 沈阳. 中国科学院沈阳自动化研究所,2021.
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