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Volume 15
Issue 9
10.3390/computers15090561
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Lane Detection Algorithm Based on Improved YOLOv8

by
Ke Zheng
1,*,
Jincheng Jiang
2,
Zhixue Liang
2,
Yoo Youngjae
3 and
Yufeng Wang
1
1
Academy of Electronic and Information Science, Nanyang Institute of Technology, Nanyang 473004, China
2
School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China
3
College of Liberal Arts and Social Science, Kyungnam University, Changwon 51734, Gyeongsangnam-do, Republic of Korea
*
Author to whom correspondence should be addressed.
Computers 2026, 15(9), 561; https://doi.org/10.3390/computers15090561
Submission received: 10 July 2026 / Revised: 22 August 2026 / Accepted: 24 August 2026 / Published: 26 August 2026
(This article belongs to the Special Issue Advances in Computer Vision: Models, Learning, and Inference)
Versions Notes

Abstract

Lane detection is a core perception task for Advanced Driver Assistance Systems (ADAS) and autonomous driving. Current methods struggle to balance accuracy, model complexity and inference efficiency: high-precision models rely on heavy modules with excessive computation, while lightweight ones suffer from weak feature extraction and low precision. To alleviate this inherent trade-off, we propose YOLOv8n-LaneDG based on YOLOv8n-seg. We design a dual-path gated fusion block to strengthen lane features and an efficient upsampling convolution block to reduce computational overhead, and we further design a weighted continuity loss to preserve lane structural integrity. Evaluated on TuSimple, our method lifts mAP@0.5 from 74.3% to 95.2%. It outperforms mainstream lightweight models and matches heavy YOLOv8s-seg with far fewer parameters, delivering a high-precision, deployable lane detection solution for vehicle-end platforms.
Keywords: autonomous driving; Dual-path Gated Fusion Block (DGFBlock); Efficient Upsampling Convolution Block (EUCB); lane detection; YOLOv8n-seg autonomous driving; Dual-path Gated Fusion Block (DGFBlock); Efficient Upsampling Convolution Block (EUCB); lane detection; YOLOv8n-seg

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MDPI and ACS Style

Zheng, K.; Jiang, J.; Liang, Z.; Youngjae, Y.; Wang, Y. Lane Detection Algorithm Based on Improved YOLOv8. Computers 2026, 15, 561. https://doi.org/10.3390/computers15090561

AMA Style

Zheng K, Jiang J, Liang Z, Youngjae Y, Wang Y. Lane Detection Algorithm Based on Improved YOLOv8. Computers. 2026; 15(9):561. https://doi.org/10.3390/computers15090561

Chicago/Turabian Style

Zheng, Ke, Jincheng Jiang, Zhixue Liang, Yoo Youngjae, and Yufeng Wang. 2026. "Lane Detection Algorithm Based on Improved YOLOv8" Computers 15, no. 9: 561. https://doi.org/10.3390/computers15090561

APA Style

Zheng, K., Jiang, J., Liang, Z., Youngjae, Y., & Wang, Y. (2026). Lane Detection Algorithm Based on Improved YOLOv8. Computers, 15(9), 561. https://doi.org/10.3390/computers15090561

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