2025-4-12- Sunday
HomeIntroductionEditorial boardContact usChinese
Home > Archive>Volume 47, Issue 11, 2019 >1626-1632. DOI:10.11908/j.issn.0253-374x.2019.11.012
PDF HTML XML Export Cite reminder
A Convolutional Neural Network-Based Method for Small Traffic Sign Detection
CSTR:
[cstr]
Author:
Clc Number:

TP274+.5

  • Article
    • | |
  • Metrics
    • |
  • Reference [17]
    • |
  • Related [20]
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    In order to solve the small target detection convolutional neural network algorithm, the PVANet convolutional neural network structure was improved to conduct the experiments of traffic sign detection on the TT100K traffic sign data set. The shallow feature extraction, deep feature extraction, and HyperNet multilayer feature fusion modules were improved. The experimental results show that the improved PVANet convolutional neural network has an excellent detection ability for small target objects, and the corresponding traffic sign detection algorithm can achieve a higher mAP (mean average precision).

    Reference
    [1] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [2] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]. Computer Vision and Pattern Recognition IEEE, 2016: 770-778.
    [3] Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2017, 39(6): 1137-1146.
    [4] Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector[C]. European Conference on Computer Vision, Springer, Cham, 2016: 21-37.
    [5] Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection[C]. Computer Vision and Pattern Recognition IEEE, 2016: 779-788.
    [6] Ritter W, Stein F, Janssen R. Traffic sign recognition using color information[J]. Math Compute Model, 1995, 22(4–7): 149-161.
    [7] Priese L, Klieber J, Lakmann R, et al. New results on traffic sign recognition[C]. In: IEEE the intelligent vehicles symposium, Paris, 1994: 249-254.
    [8] Mogelmose A, Trivedi MM, Moeslund TB. Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey[J]. IEEE Trans Intell Transp Syst, 2012, 13(4): 1484-1497.
    [9] Zaklouta F, Stanciulescu B. Real-time traffic sign recognition in three stages[J]. Robot Auton Syst, 2014, 62(1): 16-24.
    [10] A. Sabbeh, Y. Al-Dunainawi, H. S. Al-Raweshidy, et al. Performance Prediction of Software Defined Network Using an Artificial Neural Network[C]. 2016 SAI Computing Conference (SAI), London, 2016: 80-84.
    [11] Sermanet P, LeCun Y, Traffic sign recognition with multiscale convolutional networks[C]. 2011 international joint conference on neural networks (IJCNN) IEEE, 2011: 2809–2813.
    [12] Aghdam HH, Heravi EJ, Puig D. A practical approach for detection and classification of traffic signs using Convolutional Neural Networks[J]. Robot Auton Syst, 2016, 84:97-112.
    [13] Hong S, Roh B, Kim K H, et al. PVANet: Deep but Lightweight Neural Networks for Real-time Object Detection[J]. Computer Vision and Pattern Recognition, 2016: 1-7.
    [14] Everingham M, Eslami S M A, Gool L V, et al. The Pascal, Visual Object Classes Challenge: A Retrospective[J]. International Journal of Computer Vision, 2015, 111(1):98-136.
    [15] Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252.
    [16] Huang J, Guadarrama S, Murphy K, et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors[J]. Computer Vision and Pattern Recognition, 2016:3296-3297.
    [17] Zhu Z, Liang D, Zhang S, et al. Traffic-Sign Detection and Classification in the Wild[C]. IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2016:2110-2118.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

ZHOU Su, ZHI Xuelei, LIU Dong, NING Hao, JIANG Lianxin, SHI Fanhuai. A Convolutional Neural Network-Based Method for Small Traffic Sign Detection[J].同济大学学报(自然科学版),2019,47(11):1626~1632

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:April 23,2019
  • Revised:September 30,2019
  • Adopted:September 19,2019
  • Online: December 05,2019
Article QR Code

Youare the 28456 visitor to this site

Copyright:JOURNAL OF TONGJI UNIVERSITY     Director:The Ministry of Education    The host:Tongji university.

Address:上海市四平路1239号同济大学内    Postcode:200092    TEL:86-21-65982344     E-mail:zrxb@tongji.edu.cn

Supported byBeijing E-Tiller Technology Development Co.,Ltd.