DOI QR코드

DOI QR Code

교통 신호 인식을 위한 경량 잔류층 기반 컨볼루션 신경망

Lightweight Residual Layer Based Convolutional Neural Networks for Traffic Sign Recognition

  • ;
  • 류재흥 (전남대학교 컴퓨터공학과)
  • 투고 : 2022.01.03
  • 심사 : 2022.02.17
  • 발행 : 2022.02.28

초록

교통 표지 인식은 교통 관련 문제를 해결하는 데 중요한 역할을 한다. 교통 표지 인식 및 분류 시스템은 교통안전, 교통 모니터링, 자율주행 서비스 및 자율주행 차의 핵심 구성 요소이다. 휴대용 장치에 적용할 수 있는 경량 모델은 설계 의제의 필수 측면이다. 우리는 교통 표지 인식 시스템을 위한 잔여 블록이 있는 경량 합성곱 신경망 모델을 제안한다. 제안된 모델은 공개적으로 사용 가능한 벤치마크 데이터에서 매우 경쟁력 있는 결과를 보여준다.

Traffic sign recognition plays an important role in solving traffic-related problems. Traffic sign recognition and classification systems are key components for traffic safety, traffic monitoring, autonomous driving services, and autonomous vehicles. A lightweight model, applicable to portable devices, is an essential aspect of the design agenda. We suggest a lightweight convolutional neural network model with residual blocks for traffic sign recognition systems. The proposed model shows very competitive results on publicly available benchmark data.

키워드

참고문헌

  1. S. K. Berkaya, H. Gunduz, O. Ozsen, C. Akinlar, and S. Gunal, "On circular traffic sign detection and recognition," Expert Systems with Applications, vol. 48, 2016, pp. 67-75. https://doi.org/10.1016/j.eswa.2015.11.018
  2. Y. Yang, H. Luo, H. Xu, and F. Wu, "Toward real-time traffic sign detection and classification," IEEE Trans. on Intelligent transportation systems, vol. 17 no. 7, 2015, pp. 2022-2031. https://doi.org/10.1109/TITS.2015.2482461
  3. Y. LeCun and Y. Bengio, "Convolutional networks for images, speech and time series," In M. A. Arbib, ed., The handbook of brain theory and neural networks, Cambridge, MA, USA, MIT Press, 1995, pp. 276-279.
  4. G. Loy and N. Barnes, "Fast shape-based road sign detection for a driver assistance system," 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, vol. 1, 2004, pp. 70-75.
  5. W. G. Shadeed, D. I. A, A. Nadi, and M. J. Mismar, "Road traffic sign detection in color images," IEEE conf.: electronics circuits and systems, vol. 2, 2003, pp. 14-17.
  6. W. J. Yang, C. C. Luo, P. C. Chung, and J. F. Yang, "Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition," Lecture Notes in Networks and Systems, vol 69, 2020, pp. 237-249. https://doi.org/10.1007/978-3-030-12388-8_17
  7. N. Barnes, G. Loy, and D. Shaw, "The regular polygon detector," Pattern Recognition, vol. 43, 2010, pp. 592-602. https://doi.org/10.1016/j.patcog.2009.09.008
  8. D. L. Escalera, L. Moreno, M. Salichs, and J. Armiongol, "Road traffic sign detection and classification," IEEE Trans. on Industrial Electronics, vol. 44, 1997, pp. 848-859. https://doi.org/10.1109/41.649946
  9. J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, "The German traffic sign recognition benchmark: a multi-class classification competition," In IEEE. Int. joint conf. on neural networks, San Jose, CA, USA, 2011, pp. 1453-1460.
  10. S. Maldonado-Bascon, S. Lafuente-Arroyo, P. Gil-Jimenez, H. Gomez-Moreno, and F. Lopez-Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines," IEEE Trans. on Intelligent Transportation Systems, vol. 8, 2007, pp. 264-278. https://doi.org/10.1109/TITS.2007.895311
  11. D. P. Kingma and L. J. Ba, "Adam, A method for stochastic optimization," In Proc. of the Int. Conf. for Learning Representations, San Diego, CA, USA, May 2015, pp. 1-15.
  12. M. Mathias, R. Timofte, R. Benenson, and L. Van Gool, "Traffic sign recognition - How far are we from the solution?," The 2013 Int. Joint Conf. on Neural Networks, Dallas, TX, USA, 2013, pp. 1-8.
  13. A. Bouti, M. A. Mahraz, J. Riffi, and H. Tairi, "A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network," Soft Computing, vol. 24, no. 9, 2020, pp. 6721-6733. https://doi.org/10.1007/s00500-019-04307-6
  14. D. Yasmina, R. Karima, and A. Ouahiba, "Traffic signs recognition with deep learning," in 2018 Int. Conf. on Applied Smart Systems, Medea, Algeria, Nov. 2018, pp. 1-5.
  15. W. Li, X. Li, Y. Qin, W. Song, and W. Cui, "Application of improved LeNet-5 network in traffic sign recognition," in Proc. of the 3rd Int. Conf. on Video and Image Processing, Shanghai China, Dec. 2019, pp. 13-18.
  16. W. Li, D. Li, and S. Zeng, "Traffic sign recognition with a small convolutional neural network," IOP Conf. Series: Materials Science and Engineering, vol. 688, article 044034, 2019. https://doi.org/10.1088/1757-899x/688/4/044051
  17. J. Li and Z. Wang, "Real-time traffic sign recognition based on efficient CNNs in the wild," IEEE Trans. on Intelligent Transportation Systems, vol. 20, no. 3, 2019. pp. 975-984. https://doi.org/10.1109/tits.2018.2843815
  18. S. Yin, J. Deng, D. Zhang, and J. Du, "Traffic sign recognition based on deep convolutional neural network," in Computer Vision: Second CCF Chinese Conf., CCCV 2017, Tianjin, China, 2017, pp. 685-695.
  19. S. Salimov and J. Yoo, "A Design of Small Scale Deep CNN Model for Facial Expression Recognition using the Low Resolution Image Datasets," J. of the Korea Institute of Electronic Communication Sciences, vol. 16 no. 1, 2021, pp. 75-80. https://doi.org/10.13067/JKIECS.2021.16.1.75
  20. J. Yoo, "An Extension of Unified Bayesian Tikhonov Regularization Method and Application to Image Restoration," J. of the Korea Institute of Electronic Communication Sciences, vol. 15, no. 01, 2020, pp. 161-166.
  21. J. Yoo, "A Unified Bayesian Tikhonov Regularization Method for Image Restoration," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 11, 2016, pp. 1129-1134. https://doi.org/10.13067/JKIECS.2016.11.11.1129
  22. J. Yoo, "Self-Regularization Method for Image Restoration," J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 1, 2016, pp. 45-52. https://doi.org/10.13067/JKIECS.2016.11.1.45
  23. S. Park, "Optimal QP Determination Method for Adaptive Intra Frame Encoding," J. of the Korea Institute of Electronic Communication Sciences, vol. 10, no. 9, 2015, pp. 1009-1018. https://doi.org/10.13067/JKIECS.2015.10.9.1009