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SES-YOLOv8n: automatic driving object detection algorithm based on improved YOLOv8

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Abstract

The perception system in autonomous driving mainly uses object detection algorithms to obtain the distribution of obstacles for recognition and analysis. Current object detection algorithms have rapidly developed, but it is challenging to balance the requirements of real-time detection and high detection accuracy in actual application scenarios. To solve the above problems, this paper uses YOLOv8n as the baseline model and proposes an object detection network named SES-YOLOv8n. Firstly, the SPPF module in the network was replaced by the SPPCSPC module to enhance further the model’s fusion ability under feature maps of different scales. The efficient multi-scale attention module EMA is introduced into the C2F module of the backbone network, which improves the perception ability in critical areas and the efficiency of feature extraction. Finally, the SPD-Conv module is used to replace part of the convolution modules in the backbone network to replace the downsampling operation, which can more effectively retain the feature information and improve the network’s accuracy and learning ability. Experimental results on the KITTI dataset and BDD100K dataset show that the average accuracy of the improved network model reaches 92.7% and 41.9%, which is 3.4% and 5.0% higher than that of the baseline model and is significantly better than the baseline model. This model can realize real-time image processing in general scenes based on ensuring high detection accuracy.

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Data availability

Image dataset used in this research is available online.

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Funding

Research on Key Technologies of Intelligent Equipment for Mine Powered by Pure Clean Energy, Natural Science Foundation of Hebei Province, F2021402011.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, 056038, China

    Yang Sun

  2. College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, 056038, China

    Yuhang Zhang, Jianhua Guo, Jiushuai Zheng & Haonan Ning

  3. Jizhong Energy Fengfeng Group Co. LTD, 16 Unicom South Road, Handan City, Hebei Province, China

    Haiyang Wang

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  1. Yang Sun
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  6. Haonan Ning
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All authors have contributed their unique insights to the research concept, and after review and discussion, they unanimously approved the content of the final manuscript.

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Correspondence to Yuhang Zhang.

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Sun, Y., Zhang, Y., Wang, H. et al. SES-YOLOv8n: automatic driving object detection algorithm based on improved YOLOv8. SIViP (2024). https://doi.org/10.1007/s11760-024-03003-9

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