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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Overview of Image-based Object Recognition AI technology for Autonomous Vehicles

자율주행 차량 영상 기반 객체 인식 인공지능 기술 현황

  • Lim, Huhnkuk (Division of Computer Engineering, Hoseo University)
  • Received : 2021.07.18
  • Accepted : 2021.07.29
  • Published : 2021.08.31
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Abstract

Object recognition is to identify the location and class of a specific object by analyzing the given image when a specific image is input. One of the fields in which object recognition technology is actively applied in recent years is autonomous vehicles, and this paper describes the trend of image-based object recognition artificial intelligence technology in autonomous vehicles. The image-based object detection algorithm has recently been narrowed down to two methods (a single-step detection method and a two-step detection method), and we will analyze and organize them around this. The advantages and disadvantages of the two detection methods are analyzed and presented, and the YOLO/SSD algorithm belonging to the single-step detection method and the R-CNN/Faster R-CNN algorithm belonging to the two-step detection method are analyzed and described. This will allow the algorithms suitable for each object recognition application required for autonomous driving to be selectively selected and R&D.

객체 인식이란 하나의 특정 이미지를 입력했을 때, 주어진 이미지를 분석하여 특정한 객체(object)의 위치(location)와 종류(class)를 파악하는 것이다. 최근 객체 인식 기술이 적극적으로 접목되는 분야 중 하나는 자율주행 차량이라 할 수 있고, 본 논문에서는 자율주행 차량에서 영상 기반의 객체 인식 인공지능 기술에 대해 기술한다. 영상 기반 객체 검출 알고리즘은 최근 두 가지 방법(단일 단계 검출 방법 및 두 단계 검출 방법)으로 좁혀지고 있는데, 이를 중심으로 분석 정리하고자 한다. 두 가지 검출 방법의 장단점을 분석 제시하고, 단일 단계 검출 방법에 속하는 YOLO/SSD 알고리즘과 두 단계 검출 방법에 속하는 R-CNN/Faster R-CNN 알고리즘에 대해 분석 기술한다. 이를 통해 자율주행에 필요한 각 객체 인식 응용에 적합한 알고리즘이 선별적으로 선택되어 연구개발 되어질 수 있기를 기대한다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(Ministry of Science and ICT) (No. 2021R1A2C1010481)

References

  1. K. Park, M. Lee, S. Noh, S. Park, C. Lee, J. Woo, and K. Jung, "Object recognition deep learning network and implementation method for autonomous vehicles," The Magazine of the IETE, vol. 46, no. 1, pp. 18-29, 2019.
  2. J. Park, "Artificial Intelligence Technology Trends in Autonomous Vehicles," IITP Weekly Technology Trends, pp. 1-16, Aug. 2018.
  3. N. Gawk, "Image Recognition Trends for Autonomous Vehicles," Auto Journal, Jun. 2018.
  4. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.
  5. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," European Conference on Computer Vision (ECCV), pp. 21-37, 2016.
  6. H. Xu and G. Srivastava, "Automatic recognition algorithm of traffic signs based on convolution neural network," Multimedia Tools and Applications, vol. 79, Jan. 2020.
  7. J. Kim, J. Kim, G. Jang, and M. Lee, "Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection," Neural Networks, vol. 87, Mar. 2017.
  8. V. John, Z. Liu, S. Mita, C. Guo, and K. Kidono, "Real-time road surface and semantic lane estimation using deep features," Signal, Image and Video Processing, vol. 12, Mar. 2018.
  9. D. Neven, B. D. Brabandere, S. Georgoulis, M. Proesmans, and L. V. Gool, "Towards End-to-End Lane Detection: an Instance Segmentation Approach," arXiv: 1802.05591, 2018.
  10. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, pp. 1106-1114, 2012.
  11. M. Mancini, G. Costante, P. Valigi, and T. A. Ciarfuglia, "J-MOD2: Joint monocular obstacle detection and depth estimation," IEEE Robot. Autom. Lett., vol. 3, no. 3, pp. 1490-1497, 2018. https://doi.org/10.1109/lra.2018.2800083
  12. B. Huval, B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, F. Mujica, A. Coates, and A. Y. Ng, "An empirical evaluation of deep learning on highway driving," Apr. 2015.
  13. 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, pp. 580-587, Jun. 2014.
  14. 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, 2017. https://doi.org/10.1109/TPAMI.2016.2577031