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Metallographic Analysis of the Percentage of Carbon in the Test Tube Based on Artificial Vision Read chapter Read first chapter

2021 | OriginalPaper | Chapter

34. Deep Learning for Robot Vision

Authors : Mamilla Keerthikeshwar, S. Anto

Published in: Intelligent Manufacturing and Energy Sustainability

Publisher: Springer Singapore

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Abstract

Deep learning comes under a class of machine learning where we use it for extremely high-level output, like recognition of images, etc. It has been used in pattern recognition over a vast area such as handmade crafts to extract the data from learning procedures. At present, it has gained a great significance in robot vision. In this paper, we show how neural networks play a vital role in robot vision. Image segmentation, which is the initial step, is used to preprocess the images and videos. The multilayered artificial neural networks have a lot more applications. It can be applied in drug detection, military bases, and many more. The main objective of this paper is to review how deep learning algorithms and deep nets can be used in various areas of robot vision. There are some predefined deep learning algorithms that are available in the market, which are used here to perform this comparative study. These will help us to have a clear insight while building vision systems using deep learning.

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Literature
1.
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y.-W. Tai, L. Xu, Accurate single stage detector using recurrent rolling convolution, in CVPR (2017)
2.
3.
G. Wang, P. Luo, L. Lin, X. Wang, Learning object interactions and descriptions for semantic image segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 5859–5867
4.
Y.H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, G. Toderici, Beyond short snippets: deep networks for video classification, in IEEE Conference on Computer Vision and Pattern Recognition[CVPR] (2015), pp. 4694–4702
5.
Y. Chen, C. Shen, X.-S. Wei, L. Liu, J. Yang, Adversarial PoseNet: a structure-aware convolutional network for human pose estimation. arXiv:​1705.​00389
6.
7.
A. Kendall, R. Cipolla, Modelling uncertainty in deep learning for camera relocalization, in IEEE International Conference on Robotics and Automation [ICRA] (May 2016)
8.
J. Schlosser, C.K. Chow, Z. Kira. Fusing LIDAR and images for pedestrian detection using convolutional neural networks, in IEEE International Conference on Robotics and Automation [ICRA] (May 2016)
9.
10.
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in CVPR 2015 [best paper honorable mention]
11.
S. Yang, D. Maturana, S. Scherer, Real-time 3D scene layout from a single image using convolutional neural networks, in IEEE International Conference on Robotics and Automation [ICRA] (2016)
12.
Z. Cai, Q. Fan, R.S. Feris, N. Vasconcelos, A unified multi-scale deep convolutional neural network for fast object detection, in European Conference on Computer Vision (Springer International Publishing, 2016), pp. 354–370
13.
D. Guo, T. Kong, F. Sun, H. Liu, Object discovery and grasp detection with a shared convolutional neural network, in IEEE International Conference on Robotics and Automation [ICRA] (2016)
14.
N. Cruz, K. Lobos-Tsunekawa, J. Ruiz-del-Solar, Using convolutional neural networks in robots with limited computational resources: detecting NAO robots while playing soccer (2017). arXiv:​1706.​06702
15.
V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: a deep convolutional encoder-decoder architecture for image segmentation (2015). arXiv:​1511.​00561
16.
F. Yang, W. Choi, Y. Lin, Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers, in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2016)
17.
18.
R.J. Williams, J. Peng, An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Comput. 2(4), 490–501 (1990)
19.
M. Alonso Jr, Y-GAN: a generative adversarial network for depthmap estimation from multi-camera stereo images, 3 Jun 2019. arXiv preprint arXiv:​1906.​00932
20.
A. Pronobis, R.P. Rao, Learning deep generative spatial models for mobile robots, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24 Sep 2017. IEEE, pp. 755–762
21.
Z. Erickson, S. Chernova, C.C. Kemp, Semi-supervised haptic material recognition for robots using generative adversarial networks (2017). arXiv:​1707.​02796v2
22.
A. Al, M. Zain Amin, A briefly explanation of restricted boltzmann machine with practical implementation in pytorch
23.
R. Rastgoo, K. Kiani, S. Escalera, Multi-modal deep hand sign language recognition in still images using restricted boltzmann machine, in Entropy 23 Oct 2018
24.
K. Sasaki, K. Noda, T. Ogata, Visual motor integration of robot’s drawing behavior using recurrent neural network. Rob. Auton. Syst. 86, 184–195 (2016)CrossRef
25.
R. Cheng, Z. Wang, K. Fragkiadaki, Geometry-aware recurrent neural networks for active visual recognition, in 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada
26.
A. Khan, F. Zhang, Using recurrent neural networks (RNNs) as planners for bio-inspired robotic motion, in 2017 IEEE Conference on Control Technology and Applications (CCTA), 27 Aug 2017. IEEE, pp. 1025–1030
27.
N. Bin, C. Xiong, Z. Liming, X. Wendong, Recurrent neural network for robot path planning, in International Conference on Parallel and Distributed Computing: Applications and Technologies, 8 Dec 2004 (Springer, Berlin, Heidelberg, 2004), pp. 188–191
28.
A. Eitel, J.T. Springenberg, L. Spinello, M. Riedmiller, W. Burgard, Multimodal deep learning for robust RGB-D object recognition, in IEEE/RSJ International Conference on Intelligent Robots and Systems [IROS], Hamburg, Germany (2015)
29.
M. Schwarz, H. Schulz, S. Behnke, RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features, in ICRA (2015)
30.
O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation (2015). arXiv preprint arXiv:​1505.​04597
31.
C. Finn, X.Y. Tan, Y. Duan, T. Darrell, S. Levine, P. Abbeel, Deep spatial autoencoders for visuomotor learning, in IEEE International Conference on Robotics and Automation [ICRA] (2016)
32.
R. Zhang, S. Tang, Y. Zhang, J. Li, S. Yan, Perspective-adaptive convolutions for scene parsing, in IEEE Transaction on Pattern Analysis and Machine Intelligence [Early Access]
33.
P. Luo, G. Wang, L. Lin, X. Wang, Deep dual learning for semantic image segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2718–2726
Metadata
Title
Deep Learning for Robot Vision
Authors
Mamilla Keerthikeshwar
S. Anto
Copyright Year
2021
Publisher
Springer Singapore
Book
Intelligent Manufacturing and Energy Sustainability

Print ISBN: 978-981-334-442-6

Electronic ISBN: 978-981-334-443-3

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4443-3_34

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