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DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection

Author

Listed:
  • Jiaao Xia

    (School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China)

  • Meijuan Li

    (School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China)

  • Weikang Liu

    (School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China)

  • Xuebo Chen

    (School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China)

Abstract

Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the dilated spatial pyramid pooling model (DSPP) and the multiscale feature residual aggregation module (FRAM) to aggregate features at various scales. These modules excel at reducing feature noise and minimizing loss of low-level features during feature map extraction. Additionally, they enhance the model’s capability to detect objects at different scales, thereby improving the accuracy and robustness of traffic sign detection. We evaluate the performance of our method on two widely used datasets, the GTSDB and CCTSDB, and achieve impressive average accuracies (APs) of 76.13% and 78.24%, respectively. Compared with other well-known algorithms, our method shows a significant improvement in detection accuracy, demonstrating its superiority and generality. Our proposed method shows great potential for improving the performance of traffic sign detection for autonomous driving systems and will help in the development of safe and efficient autonomous driving technologies.

Suggested Citation

  • Jiaao Xia & Meijuan Li & Weikang Liu & Xuebo Chen, 2023. "DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10862-:d:1191419
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    References listed on IDEAS

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    1. Haohao Zou & Huawei Zhan & Linqing Zhang, 2022. "Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
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