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Lane Detection Algorithm Based on Improved YOLOv8
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AI
DOI：10.3390/computers15090561.png)
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Abstract
En 中文
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
Journal
C
IF:
4.2
Papers:
1.4K
Citations:
3.3K