YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems

Ming Yang; Xiangyu Fan

doi:10.62762/TETAI.2024.894227

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Volume 1, Issue 1, ICCK Transactions on Emerging Topics in Artificial Intelligence

Volume 1, Issue 1, 2024

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ICCK 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

Ming Yang 1 *

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: 22 (Source: Web of Science), 24 (Source: ), 28 (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.

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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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APA Style

Yang, M., & Fan, X. (2024). YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems. ICCK Transactions on Emerging Topics in Artificial Intelligence, 1(1), 1–16. https://doi.org/10.62762/TETAI.2024.894227

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TY  - JOUR
AU  - Yang, Ming
AU  - Fan, Xiangyu
PY  - 2024
DA  - 2024/04/07
TI  - YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 1
IS  - 1
SP  - 1
EP  - 16
DO  - 10.62762/TETAI.2024.894227
UR  - https://www.icck.org/article/abs/TETAI.2024.894227
KW  - autonomous driving
KW  - object detection
KW  - YOLOv8
KW  - real-time performance
KW  - intelligent transportation
AB  - 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.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Yang2024YOLOv8Lite,
  author = {Ming Yang and Xiangyu Fan},
  title = {YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2024},
  volume = {1},
  number = {1},
  pages = {1-16},
  doi = {10.62762/TETAI.2024.894227},
  url = {https://www.icck.org/article/abs/TETAI.2024.894227},
  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.},
  keywords = {autonomous driving, object detection, YOLOv8, real-time performance, intelligent transportation},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}

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