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http://dx.doi.org/10.6109/jkiice.2020.24.11.1437

Efficient Deep Neural Network Architecture based on Semantic Segmentation for Paved Road Detection  

Park, Sejin (Department of Computer Science and Engineering, Hanyang University)
Han, Jeong Hoon (Department of Computer Science and Engineering, Hanyang University)
Moon, Young Shik (Department of Computer Science and Engineering, Hanyang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.11, 2020 , pp. 1437-1444 More about this Journal
Abstract
With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.
Keywords
Computer vision; Deep learning; Semantic segmentation; Autonomous driving; Road detection;
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1 Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos, "A unified multi-scale deep convolutional neural network for fast object detection," in European Conference on Computer Vision, pp. 354-370, 2016.
2 T. Bengio, A. Courville, and P. Vincent "Representation learning: A review and new perspectives," IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.   DOI
3 X. Glorot, A. Bordes, and Y. Bengio, "Deep sparse rectifier neural networks," in Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315-323, 2011.
4 S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," in Advances in neural information processing systems, pp. 91-99, 2015.
5 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016.
6 J. Zhang and H.-H. Nagal, "Texture-based segmentation of road images," Proceedings of the Intelligent Vehicles Symposium, 1994.
7 S. Beucher and M. Bilodeau, "Road segmentation and obstacle detection by a fast watershed transformation," Proceedings of the Intelligent Vehicles '94 Symposium, 1994.
8 X. Yu, S. Beucher, and M. Bilodeau, "Road tracking, lane segmentation and obstacle recognition by mathematical morphology," Proceedings of the Intelligent Vehicles '92 Symposium, 1992.
9 J. Long, F. Shelhamer, and T. Darrell "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440, 2015.
10 G. L. Oliveira, W. Burgard, and T. Brox, "Efficient deep models for monocular road segmentation," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
11 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
12 G. Mattyus, S. Wang, S. Fidler, and R. Urtasun, "Hd maps: Fine-grained road segmentation by parsing ground and aerial images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3611-3619, 2016.
13 D. Levi, N. Garnett, E. Fetaya, and I. Herzlyia, "StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation," in British Machine Vision Conference, pp. 109.1-109.12, 2015.
14 V. Badrinarayanan, A. Kendall, and R. Cipolla, R. "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, pp. 2481-2495, 2017.
15 L. C. Chen, G. Papandreou, F. Schroff, and H. Adam, "Rethinking atrous convolution for semantic image segmentation," CoRR, abs/1706.05587, 2017.
16 K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask r-cnn.," in Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
17 S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shif," in International Conference on Machine Learning, pp. 448-456, 2015.
18 O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in International Conference on Medical image computing and computer-assisted intervention, pp. 234-241, 2015.
19 K. He, X. Zhang, X. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," in Proceedings of the IEEE international conference on computer vision, pp. 1026-1034, 2015.
20 D. P. Kingma and J. Ba "Adam: A Method for Stochastic Optimization," International Conference on Learning Representations (ICLR), pp. 13-24, 2015.
21 M. Cordts, M., Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, and B. Schiele, "The cityscapes dataset for semantic urban scene understanding," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213-3223, 2016.