School of EECS, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Sensors (Basel). 2021 Nov 3;21(21):7309. doi: 10.3390/s21217309.
Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.
机器人焊接通常使用基于视觉的测量来找到焊缝的正确位置。传统的机器视觉方法在许多情况下效果很好，但在制造过程或成像条件发生变化时缺乏鲁棒性。虽然监督深度学习网络在许多实际测量应用中提高了准确性和鲁棒性，但它们的成功依赖于标记数据。在本文中，我们采用半监督学习来同时提高准确性和鲁棒性，同时避免由领域专家进行昂贵且耗时的标记工作。虽然存在用于各种图像分类任务的半监督学习方法，但我们提出了一种用于焊接机器人焊缝位置关键点检测的新型半监督算法。我们证明，我们的方法可以在仅使用十五张标记图像的情况下稳健地工作。此外，我们的方法利用全图像分辨率来提高焊缝位置关键点检测的准确性。
