Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 210-224
GAO Tao, XING Ke, LIU Zhan-wen, CHEN Ting, YANG Zhao-chen, LI Yong-hui. Traffic sign detection algorithm based on pyramid multi-scale fusion[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 210-224. doi: 10.19818/j.cnki.1671-1637.2022.03.017
Citation: GAO Tao, XING Ke, LIU Zhan-wen, CHEN Ting, YANG Zhao-chen, LI Yong-hui. Traffic sign detection algorithm based on pyramid multi-scale fusion[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 210-224. doi: 10.19818/j.cnki.1671-1637.2022.03.017
shu

Traffic sign detection algorithm based on pyramid multi-scale fusion

doi: 10.19818/j.cnki.1671-1637.2022.03.017
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Xi'an Branch, China Mobile Group Shaanxi Co., Ltd., Xi'an 710075, Shaanxi, China

  • 3.

    School of Electrical and Information Engineering, The University of Sydney, Sydney NSW2006, New South Wales, Australia

Funds:

National Key Research and Development Program of China 2019YFE0108300

National Key Research and Development Program of China 2018YFB1600600

National Natural Science Foundation of China 52172379

National Natural Science Foundation of China 62001058

Fundamental Research Funds for the Central Universities 310833160212

Fundamental Research Funds for the Central Universities 300102242901

More Information
  • Author Bio:

    GAO Tao(1980-), male, professor, PhD, gtnwpu@126.com

    CHEN Ting(1982-), female, associate professor, PhD, tchen@chd.edu.cn

  • Received Date: 2022-02-13
  • Publish Date: 2022-06-25
    Abstract
  • In order to address the problems of misdetection and missing detection for small target traffic signs in traditional traffic sign detection algorithms, a traffic sign detection algorithm based on pyramidal multi-scale fusion was proposed. To improve the feature extraction capability of the algorithm for traffic signs, the residual structure of ResNet was adopted to build the backbone network of the algorithm, and, the number of shallow convolutional layers of the backbone network was increased to extract more accurate semantic information of smaller scale traffic signs. Based on the idea of feature pyramid network, four different prediction scales were introduced in the detection network to enhance the fusion between deep and shallow features. To further improve the detection accuracy of the algorithm, the GIoU loss function was introduced to localize the anchor boxes of traffic signs. Meanwhile, the k-means algorithm was introduced to cluster the traffic sign label information and generate more accurate prior bounding boxes. In order to verify the generalization of the algorithm and solve the problem of inter-class imbalance of TT100K data set used in the experiment, the data set was enhanced and expanded. Experimental results show that the accuracy, recall and average accuracy of the proposed algorithm are 86.7%, 89.4% and 87.9%, respectively, significantly improving compared with traditional target detection algorithms. The adoption of multi-scale fusion detection mechanism, GIoU loss function and k-means improves the detection performance of the algorithm to different degrees, and its precision improves by 4.7%, 1.8% and 1.2%, respectively. The algorithm has better performance in the detection of traffic signs under different scales, and its recall rate is 90%, 93% and 88% under the scales of (0, 32], (32, 96] and (96, 400] in TT100K dataset, respectively. Comparing with YOLOv3, the proposed algorithm can correctly locate and classify traffic signs under the interference of different weather, noise and geometric transformation, which proves that the proposed algorithm has good robustness and generalization, and is suitable for road traffic sign detection. 7 tabs, 18 figs, 30 refs.

     

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    • References(30)
  • [1]
    马永杰, 马芸婷, 程时升, 等. 基于改进YOLOv3模型与Deep-SORT算法的道路车辆检测方法[J]. 交通运输工程学报, 2021, 21(2): 222-231. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102022.htm

    MA Yong-jie, MA Yun-ting, CHENG Shi-sheng, et al. Road vehicle detection method based on improved YOLOv3 model and deep-SORT algorithm[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 222-231. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102022.htm
    [2]
    DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//IEEE. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2005: 886-893.
    [3]
    PICCIOLI G, DE MICHEL E, PARODI P, et al. Robust method for road sign detection and recognition[J]. Image and Vision Computing, 1996, 14(3): 209-223. doi: 10.1016/0262-8856(95)01057-2
    [4]
    梁敏健, 崔啸宇, 宋青松, 等. 基于HOG-Gabor特征融合与Softmax分类器的交通标志识别方法[J]. 交通运输工程学报, 2017, 17(3): 151-158. doi: 10.3969/j.issn.1671-1637.2017.03.016

    LIANG Min-jian, CUI Xiao-yu, SONG Qing-song, et al. Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 151-158. (in Chinese) doi: 10.3969/j.issn.1671-1637.2017.03.016
    [5]
    DENG Li, ABDEL-HAMID O, YU Dong. A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion[C]//IEEE. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE, 2013: 6669-6673.
    [6]
    马永杰, 程时升, 马芸婷, 等. 卷积神经网络及其在智能交通系统中的应用综述[J]. 交通运输工程学报, 2021, 21(4): 48-71. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202104006.htm

    MA Yong-jie, CHENG Shi-sheng, MA Yun-ting, et al. Review of convolutional neural network and its application in intelligent transportation system[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 48-71. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202104006.htm
    [7]
    GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE. 27th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2014: 580-587.
    [8]
    GIRSHICK R. Fast R-CNN[C]//IEEE. 28th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 1440-1448.
    [9]
    REN Shao-qing, HE Kai-ming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
    [10]
    ZHU Zhe, LIANG Dun, ZHANG Song-hai, et al. Traffic- sign detection and classification in the wild[C]//IEEE. 29th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 2110-2118.
    [11]
    LIANG Zhen-wen, SHAO Jie, ZHANG Dong-yang, et al. Traffic sign detection and recognition based on pyramidal convolutional networks[J]. Neural Computing and Applications, 2020, 32(11): 6533-6543. doi: 10.1007/s00521-019-04086-z
    [12]
    LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//IEEE. 30th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 2117-2125.
    [13]
    周苏, 支雪磊, 刘懂, 等. 基于卷积神经网络的小目标交通标志检测算法[J]. 同济大学学报(自然科学版), 2019, 47(11): 1626-1632. doi: 10.11908/j.issn.0253-374x.2019.11.012

    ZHOU Su, ZHI Xue-lei, LIU Dong, et al. A convolutional neural network-based method for small traffic sign detection[J]. Journal of Tongji University (Natural Science), 2019, 47(11): 1626-1632. (in Chinese) doi: 10.11908/j.issn.0253-374x.2019.11.012
    [14]
    HONG S, ROH B, KIM H, et al. PVANet: lightweight deep neural networks for real-time object detection[EB/OL]. (2016-12-09)[2022-07-02]. https://arxiv.org/abs/1611.08588.
    [15]
    REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// IEEE. 29th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 779-788.
    [16]
    REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//IEEE. 30th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 7263-7271.
    [17]
    REDMON J, FARHADI A. YOLOv3: an incremental improvement[R]. Ithaca: Cornell University, 2018.
    [18]
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Springer. 14th European Conference on Computer Vision. Berlin: Springer, 2016: 21-37.
    [19]
    FU Cheng-yang, LIU Wei, RANGA A, et al. DSSD: Deconvolutional single shot detector[J]. arXiv, 2017: 20200017371.
    [20]
    RAJENDRAN S P, SHINE L, PRADEEP R, et al. Real-time traffic sign recognition using yolov3 based detector[C]//IEEE. 10th International Conference on Computing, Communication and Networking Technologies. New York: IEEE, 2019: 1-7.
    [21]
    STALLKAMP J, SCHLIPSING M, SALMEN J, et al. The German traffic sign recognition benchmark: A multi-class classification competition[C]//IEEE. 2011 International Joint Conference on Neural Networks. New York: IEEE, 2011: 1453-1460.
    [22]
    ZHANG Hui-bing, QIN Long-fei, LI Jun, et al. Real-time detection method for small traffic signs based on YOLOv3[J]. IEEE Access, 2020, 8: 64145-64156. doi: 10.1109/ACCESS.2020.2984554
    [23]
    ZHANG Hong-yi, CISSE M, DAUPHIN Y N, et al. Mixup: beyond empirical risk minimization[EB/OL]. (2018-04-27)[2022-07-02]. https://arxiv.org/abs/1710.09412.
    [24]
    WU Yi-qiang, LI Zhi-yong, CHEN Ying, et al. Real-time traffic sign detection and classification towards real traffic scene[J]. Multimedia Tools and Applications, 2020, 79(25/26): 18201-18219.
    [25]
    HE Kai-ming, ZHANG Xiang-yu, REN Shao-qing, et al. Deep residual learning for image recognition[C]//IEEE. 29th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.
    [26]
    REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: A metric and a loss for bounding box regression[C]//IEEE. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2019: 658-666.
    [27]
    高涛, 刘梦尼, 陈婷, 等, 等. 结合暗亮通道先验的远近景融合去雾算法[J]. 西安交通大学学报, 2021, 55(10): 78-86. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT202110009.htm

    GAO Tao, LIU Meng-ni, CHEN Ting, et al. A far and near scene fusion defogging algorithm based on the prior of dark-light channel[J]. Journal of Xi'an Jiaotong University, 2021, 55(10): 78-86. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT202110009.htm
    [28]
    ŽALIK K R. An efficient k'-means clustering algorithm[J]. Pattern Recognition Letters, 2008, 29(9): 1385-1391. doi: 10.1016/j.patrec.2008.02.014
    [29]
    LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]//Springer. 13th European conference on computer vision. Berlin: Springer, 2014: 740-755.
    [30]
    EVERINGHAM M, VAN L, CHRISTOPHER W, et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.
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