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Driving risks from light pollution: an improved YOLOv8 detection network for high beam vehicle image recognition

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Abstract

The effective detection of high beam vehicles is useful to reduce the occurrence of traffic accidents and light pollution at night. It is time-consuming and laborious to detect the high beam vehicles by human. We propose a model Global Attention Content-Aware ReAssembly of FEatures Small Target Detection based on YOLOv8 (GCS-YOLOv8) to improve the efficiency of high beam vehicles detection. Firstly, a cross-stage feature fusion global attention module (CGM) is designed in the backbone to reduce the information loss during down-sampling and magnify global dimension-interactive features. Secondly, a Convolution-1*1 Content-Aware ReAssembly of FEatures Module (CCM) is designed in the neck to reconstruct subtle feature maps of high beam vehicles. Meanwhile, a CCM Small Target Detection Module (CSM) is designed in the neck to derive the feature maps with small receptive field. Finally, we set up a high beam vehicles dataset. The model GCS-YOLOv8 achieves the mean average precision (mAp) of 94% on the test set, and it is higher than other models. In addition, multiple ablation experiments are done to prove the effectiveness of our model GCS-YOLOv8.

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No datasets were generated or analyzed during the current study.

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Acknowledgements

This work is supported by R&D Program of Beijing Municipal Education Commission (KM202410017006, KM202210017006), Beijing Digital Education Research Project (BDEC2022619048), Ningxia Natural Science Foundation General Project (2022AAC03757, 2023AAC03889) and Ministry of Education Industry-School Cooperative Education Project (220607039172210, 22107153134955).

Funding

This study was funded by the General Project of Science and Technology Plan of Beijing Municipal Education Commission, KM202210017006.

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Authors and Affiliations

  1. Beijing Institute of Petrochemical Technology, Beijing, 102617, China

    Lili Zhang, Ke Zhang, Kang Yang, Wei Wei, Jing Li, Yucheng Han & Xudong Yang

  2. Science and Technology on Complex Aviation Systems Simulation Laboratory, Beijing, 100076, China

    Hongxin Tan

  3. China Fire and Rescue Institute, Beijing, 102202, China

    Pei Yu

Authors
  1. Lili Zhang
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  2. Ke Zhang
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  3. Kang Yang
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  4. Wei Wei
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  5. Jing Li
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  6. Hongxin Tan
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  7. Pei Yu
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  8. Yucheng Han
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  9. Xudong Yang
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Contributions

Conceptualization was performed by Lili Zhang; methodology was conducted by Ke Zhang; software and validation were developed by Li Jing; formal analysis and investigation were carried out by Wei Wei; resources were provided by Xudong Yang; data curation was contributed by Hongxin Tan; writing—original draft preparation was prepared by Yucheng Han; writing—review and editing were drafted by KangYang and Lili Zhang; visualization was analyzed by Kang Yang; project administration was approved by Pei Yu. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hongxin Tan.

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Zhang, L., Zhang, K., Yang, K. et al. Driving risks from light pollution: an improved YOLOv8 detection network for high beam vehicle image recognition. J Supercomput 81, 275 (2025). https://doi.org/10.1007/s11227-024-06809-z

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