Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Efficient Deep Neural Network Architecture based on Semantic Segmentation for Paved Road Detection

효율적인 비정형 도로영역 인식을 위한 Semantic segmentation 기반 심층 신경망 구조

  • 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)
  • Received : 2020.07.13
  • Accepted : 2020.07.16
  • Published : 2020.11.30
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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.

컴퓨터 비전 시스템의 발달로 보안, 생체인식, 의료영상, 자율주행 등의 분야에 많은 발전이 있었다. 자율주행 분야에서는 특히 딥러닝을 이용한 객체인식, 탐지 기법이 주로 사용되는데, 자동차가 갈 수 있는 영역을 판단하기 위한 도로영역 인식이 특히 중요한 문제이다. 도로 영역은 일반적인 객체탐지에서 활용되는 사각영역인식과는 달리 비정형적인 형태를 띠므로, ROI 기반의 객체인식 구조는 적용할 수 없다. 본 논문에서는 Semantic segmentation 기법을 사용한 비정형적인 도로영역 인식에 맞는 심층 신경망 구조를 제안한다. 또한 도로영역에 특화된 네트워크 구조인 Multi-scale semantic segmentation 기법을 사용하여 성능이 개선됨을 입증하였다.

Keywords

References

  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. https://doi.org/10.1109/TPAMI.2013.50
  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. Zhang and H.-H. Nagal, "Texture-based segmentation of road images," Proceedings of the Intelligent Vehicles Symposium, 1994.
  6. S. Beucher and M. Bilodeau, "Road segmentation and obstacle detection by a fast watershed transformation," Proceedings of the Intelligent Vehicles '94 Symposium, 1994.
  7. 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.
  8. 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.
  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. 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.
  12. 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.
  13. 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.
  14. L. C. Chen, G. Papandreou, F. Schroff, and H. Adam, "Rethinking atrous convolution for semantic image segmentation," CoRR, abs/1706.05587, 2017.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. D. P. Kingma and J. Ba "Adam: A Method for Stochastic Optimization," International Conference on Learning Representations (ICLR), pp. 13-24, 2015.
  20. 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.
  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.