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YOLO-SG: Small traffic signs detection method in complex scene

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Abstract

Fast and accurate detection of traffic signs is crucial for the development of intelligent transportation systems. To address the issue of false detection and missing detection of small traffic signs in complex scenes, this paper proposes a YOLO-SG model based on YOLOv5. The YOLO-SG approach employs SPD-Conv as a down-sampling structure to mitigate the loss of feature information during the down-sampling process. This enhances the detection performance of small objects in complex scenes and improves the generalization and robustness of the model. The feature extraction architecture uses GhostNet, which effectively reduces the number of model parameters and weight, enhancing the feasibility of practical model deployment. Furthermore, this study optimizes the output feature structure by introducing a small object detection layer and removing the large object detection layer, enabling the detection of small objects. Extensive experiments conducted on the GTSDB and TT100K datasets demonstrate that YOLO-SG exhibits excellent detection performance. On the GTSDB dataset, YOLO-SG achieved a 2.3% increase in mAP compared to the baseline network, while reducing the number of parameters by 42%. Similarly, on the TT100K dataset, YOLO-SG increased mAP by 6.3% and reduced the number of parameters by 43%. These experimental results showcase the effectiveness and accuracy of YOLO-SG, particularly in detecting small traffic signs.

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The GTSDB and TT100K datasets used in this paper are open, which can be downloaded from the Internet.

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Funding

Funding for this study was received from the Youth Program of Shaanxi Province: 2022JQ-624; the China University Industry Research and Innovation Fund: 2021ALA02002; the Higher Education Teaching Reform Research Project of China Textile Industry Association: 2021BKJGLX004; and the Higher Education Research Project of Xi’an Polytechnic University: 20GJ05.

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Author notes

  1. Yanjiang Han and Fengping Wang contributed equally to this work.

Authors and Affiliations

  1. Xi’an Polytechnic University, Xi’an, 710600, Shaanxi, China

    Yanjiang Han, Fengping Wang, Wei Wang, Xiangyu Li & Jianyang Zhang

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  1. Yanjiang Han
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  2. Fengping Wang
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  3. Wei Wang
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  4. Xiangyu Li
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  5. Jianyang Zhang
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Contributions

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

Corresponding author

Correspondence to Wei Wang.

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Han, Y., Wang, F., Wang, W. et al. YOLO-SG: Small traffic signs detection method in complex scene. J Supercomput 80, 2025–2046 (2024). https://doi.org/10.1007/s11227-023-05547-y

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