Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.8.1117

Overview of Image-based Object Recognition AI technology for Autonomous Vehicles  

Lim, Huhnkuk (Division of Computer Engineering, Hoseo University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.8, 2021 , pp. 1117-1123 More about this Journal
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.
Keywords
Object detection; Autonomous vehicle; Image-based AI; Single-step detection; Two-step detection;
Citations & Related Records
연도 인용수 순위
  • Reference
1 N. Gawk, "Image Recognition Trends for Autonomous Vehicles," Auto Journal, Jun. 2018.
2 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.
3 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.
4 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.
5 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.
6 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.
7 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.
8 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.
9 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.
10 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.   DOI
11 H. Xu and G. Srivastava, "Automatic recognition algorithm of traffic signs based on convolution neural network," Multimedia Tools and Applications, vol. 79, Jan. 2020.
12 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.   DOI
13 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.
14 J. Park, "Artificial Intelligence Technology Trends in Autonomous Vehicles," IITP Weekly Technology Trends, pp. 1-16, Aug. 2018.