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    Volume 1, Issue 1, IECE Transactions on Emerging Topics in Artificial Intelligence
    Volume 1, Issue 1, 2024
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    Teerath Kumar
    Teerath Kumar
    National College of Ireland, Ireland
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    IECE Transactions on Emerging Topics in Artificial Intelligence, 2024, Volume 1, Issue 1: 1-16

    Free to Read | Research Article | Feature Paper | 07 April 2024
    YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
    by
    IECE Author Ming Yang 1 *
    IECE Author Xiangyu Fan 1
    1 College of Architecture and Design, Tongmyong University, Busan 608-711, Korea
    * Corresponding Author: Ming Yang, [email protected]
    DOI: https://doi.org/10.62762/TETAI.2024.894227
    Received: 17 December 2023, Accepted: 02 April 2024, Published: 07 April 2024  
    Cited by: 11  (Source: Web of Science) , 11  (Source: Google Scholar)
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    Abstract
    With the rapid development of autonomous driving technology, the demand for real-time and efficient object detection systems has been increasing to ensure vehicles can accurately perceive and respond to the surrounding environment. Traditional object detection models often suffer from issues such as large parameter sizes and high computational resource consumption, limiting their applicability on edge devices. To address this issue, we propose a lightweight object detection model called YOLOv8-Lite, based on the YOLOv8 framework, and improved through various enhancements including the adoption of the FastDet structure, TFPN pyramid structure, and CBAM attention mechanism. These improvements effectively enhance the performance and efficiency of the model. Experimental results demonstrate significant performance improvements of our model on the NEXET and KITTI datasets. Compared to traditional methods, our model exhibits higher accuracy and robustness in object detection tasks, better addressing the challenges in fields such as autonomous driving, and contributing to the advancement of intelligent transportation systems.
    Graphical Abstract
    YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
    Keywords
    autonomous driving
    object detection
    YOLOv8
    real-time performance
    intelligent transportation
    Funding
    This work was supported without any funding.
    Cite This Article
    APA Style
    M,Yang & X,Fan. (2024). YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems.IECE Transactions on Emerging Topics in Artificial Intelligence ,1(1),1-16. https://doi.org/10.62762/TETAI.2024.894227
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    IECE Transactions on Emerging Topics in Artificial Intelligence

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