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RBS-YOLO: a vehicle detection algorithm based on multi-scale feature extraction

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Abstract

In autonomous driving, vehicles are recognized by computer vision and image processing to reduce the risk of accidents. However, traditional vehicle detection algorithms often struggle to deal with the vehicle occlusion problem effectively, necessitating the modification of feature map size as vehicle sizes vary. To address these issues, we proposed RBS-YOLO, a vehicle detection model based on YOLOv5. First, the ResFusion module was designed to expand the model’s receptive field and capture features at various scales. Second, using a bidirectional feature pyramid network, we enhanced the inclusiveness of feature information by fusing them. Finally, the SloU loss function was used instead of the CloU loss function to improve the network’s positioning accuracy and convergence speed. The experimental results indicate that the RBS-YOLO model achieves a precision rate of 97.5\(\%\) and 72.5\(\%\) on the UA-DETRAC dataset and BDD-100K dataset, exceeding YOLOv5 by 1.1\(\%\) and 1.7\(\%\).

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No datasets were generated or analyzed during the current study.

Code availability

Data will be made available on reasonable request.

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Acknowledgements

This work was supported by the Fujian Province Nature Science Foundation under Grant No.2020J01813 and No.2021J011002, the Research Project on Education and Teaching Reform of Undergraduate Colleges and Universities in Fujian Province under Grant No.FBJG20210070 and No.FBJY20230170, and the 2022 Annual Project of the Fourteenth Five-Year Plan for Fujian Educational Science under Grant No.FJJKBK22-173.

Funding

Natural Science Foundation of Fujian Province (2020J01813), The Research Project on Education and Teaching Reform of Undergraduate Colleges and Universities in Fujian Province (FBJG20210070, FBJY20230170), The 2022 Annual Project of the Fourteenth Five-Year Plan for Fujian Educational Science (FJJKBK22-173).

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Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Zhangzhou, 363000, Fujian, China

    Jinghui Ren, Jingmin Yang, Wenjie Zhang & Kunhui Cai

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, 363000, Fujian, China

    Jinghui Ren, Jingmin Yang, Wenjie Zhang & Kunhui Cai

  3. Department of Electronic Engineering, Taipei University of Technology, Taipei, Taiwan, China

    Jingmin Yang

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  1. Jinghui Ren
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  2. Jingmin Yang
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  4. Kunhui Cai
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Contributions

JHR contributed to conceptualization, methodology, software, investigation, formal analysis, and writing—original draft. JMY contributed to conceptualization, funding acquisition, and writing—review and editing. WJZ contributed to funding acquisition and writing—review and editing. KHC contributed to formal analysis and methodology. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jingmin Yang.

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Ren, J., Yang, J., Zhang, W. et al. RBS-YOLO: a vehicle detection algorithm based on multi-scale feature extraction. SIViP 18, 3421–3430 (2024). https://doi.org/10.1007/s11760-024-03007-5

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