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RT-DETRmg: a lightweight real-time detection model for small traffic signs

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Abstract

In intelligent transportation systems, real-time detection performance and accuracy are essential metrics. This paper proposes a lightweight real-time detection model, RT-DETRmg, to address the challenges of false and missed detections of small traffic signs and to improve the algorithm's real-time performance. RT-DETRmg enhances the multi-scale feature extraction capability of the RT-DETR backbone network by incorporating a Multiple Scale Sequence Fusion module, which effectively integrates global and local semantic information from different scales of images. Additionally, a cascaded group attention module is utilized within an efficient hybrid encoder to reduce computational complexity, thereby enhancing real-time performance. To further optimize small object detection, a small receptive field feature layer is introduced, while a large receptive field feature layer is removed. Experimental results on the TT100K and GTSDB datasets demonstrate the superiority of RT-DETRmg over existing models. On the TT100K dataset, RT-DETRmg achieves a 2.0% improvement in mean average precision and a 6.6% increase in frames per second compared to the baseline RT-DETR model, while reducing model parameters and computational complexity. On the GTSDB dataset, RT-DETRmg further demonstrates its strong generalization ability, achieving a 2.2% improvement in the F1 score and a 1.7% increase in mean average precision compared to the baseline network. These findings highlight the effectiveness of RT-DETRmg in enhancing both detection accuracy and real-time performance of small traffic signs in diverse scenarios.

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Funding

This work was supported by the Clinical research project initiated by researchers from the Sichuan Provincial Health Commission (23LCYJ020), the Special Project for City-College Science and Technology Strategic Cooperation of Nanchong City in 2022 (22SXQT0292), and Sichuan Provincial Key R&D Plan (Major Science and Technology Project) (2022YFS0020).

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Authors and Affiliations

  1. SouthWest Petroleum University, Chengdu, 610500, Sichuan, China

    Yiqiao Wang, Jinling Chen, Yu Chen, Yanlin Su & Rong Liu

  2. State Grid Sichuan Information & Telecommunication Company, Chengdu, 610095, Sichuan, China

    Bo Yang

Authors
  1. Yiqiao Wang
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  2. Jinling Chen
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  3. Bo Yang
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  4. Yu Chen
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  5. Yanlin Su
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  6. Rong Liu
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Contributions

Y.W. and J.C. contributed to conceptualization and writing—original draft preparation; B.Y. helped with the methodology; Y.W. assisted with software; Y.C. and R.L. carried out validation; Y.W., J.C., and R.L. conducted investigation; Y.S. and Y.C. were involved in writing—review and editing; and Y.S. and Y.C. were responsible for visualization. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jinling Chen.

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Wang, Y., Chen, J., Yang, B. et al. RT-DETRmg: a lightweight real-time detection model for small traffic signs. J Supercomput 81, 307 (2025). https://doi.org/10.1007/s11227-024-06800-8

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