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YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
Research Article | Published: 07 April 2024
IECE Transactions on Emerging Topics in Artificial Intelligence Volume 1, Issue 1, 2024: 1-16
Volume 1, Issue 1, IECE Transactions on Emerging Topics in Artificial Intelligence
Volume 1, Issue 1, 2024
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National College of Ireland, Ireland
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IECE Transactions on Emerging Topics in Artificial Intelligence, Volume 1, Issue 1, 2024: 1-16

Open Access | Research Article | 07 April 2024
YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
by
Ming Yang Ming Yang 1 *
Xiangyu Fan Xiangyu Fan 1
1 College of Architecture and Design, Tongmyong University, Busan 608-711, Republic of Korea
* Corresponding Author: Ming Yang, [email protected]
DOI: 10.62762/TETAI.2024.894227
Received: 17 December 2023, Accepted: 02 April 2024, Published: 07 April 2024  
Cited by: 13  (Source: Web of Science) , 14  (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
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest. 
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Yang, M., & Fan, X. (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
Article Metrics
Citations:

Google Scholar
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Web of Science

13
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CC BY Copyright © 2024 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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