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http://dx.doi.org/10.7746/jkros.2019.14.1.001

Object Recognition and Pose Estimation Based on Deep Learning for Visual Servoing  

Cho, Jaemin (Computer Software, Korea University of Science and Technology)
Kang, Sang Seung (Electronics and Telecommunications Research Institute)
Kim, Kye Kyung (Electronics and Telecommunications Research Institute)
Publication Information
The Journal of Korea Robotics Society / v.14, no.1, 2019 , pp. 1-7 More about this Journal
Abstract
Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.
Keywords
Object Detection; Object Recognition; Deep Learning; Line Detection; Hough Transform; Perspective-Transform; Pose Estimation;
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1 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580-587, 2014.
2 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, Jun., 2017.   DOI
3 K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, DOI: 10.1109/ICCV.2017.322.
4 R. G. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, "LSD: a Line Segment Detector," Image Processing On Line, vol. 2, pp. 35-55. 2012.   DOI
5 J. A. Hartigan and M. A. Wong, "Algorithm AS 136: A k-means clustering algorithm," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100-108, 1979.
6 E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," European Conference on Computer Vision, vol. 3951, pp. 430-443, 2006
7 W. J. Wilson, C. C. W. Hulls, and G. S. Bell, "Relative end-effector control using Cartesian position based visual servoing," IEEE Transactions on Robotics and Automation, vol. 12, no. 5, pp. 684-696, Oct, 1996.   DOI
8 E. Malis and P. Rives, "Robustness of image-based visual servoing with respect to depth distribution errors," 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1056-1061, 2003.
9 F. Chaumette "Potential problems of stability and convergence in image-based and position-based visual servoing," The confluence of vision and control. Lecture Notes in Control and Information Sciences, D. J. Kriegman, G. D. Hager, A. S. Morse eds., vol. 237, Springer, London, 1998.
10 B. Nelson, N. P. Papanikolopoulous, and P. K. Khosla, "VISUAL SERVOING FOR ROBOTIC ASSEMBLY," Visual Servoing, 7th ed. World Scientific, ch. 6, pp. 139-164, 1993.
11 J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, DOI: 10.1109/CVPR.2017.690.