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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