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Weiwei Cai
Northern Arizona University
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Research Article | Feature Paper | 07 April 2024
YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 1-16, 2024 | DOI:10.62762/TETAI.2024.89422
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 improvement... More >

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