Deep Learning Based Lane Detection Using YOLOv8 Segmentation Architecture

Engineering notes

Key topics: autonomous driving, end-to-end, lane detection, object detection, adas, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The lane detection system is essential for autonomous driving and advanced driver assistance system (ADAS). A lane detection model was implemented to achieve high accuracy and robustness across different environments. The model used a deep learning lane detection framework based on the YOLOv8 segmentation architecture. This enabled simultaneous object localization and pixel mask predictions within a unified end-to-end model. Implementing this model included a systematic approach to dataset preprocessing and annotated transformations. It also involved a structured train/validation/test split. Extensive optimization and augmentation were applied to maximize both the generalizability and performance of the final model. Standard metrics were used to evaluate the model's performance. These included object detection (precision, recall, and $\mathbf{F 1}$-score) and segmentation (mean average precision, or mAP) across a range of IoU thresholds. The model's experimental mAP@50 (0.9871) and mAP@50-95 (0.8384) results showed accurate detections for strict and less strict IoU thresholds. The experimental results show a precision of 0.9579, a recall of 0.9633, and an F1-score of 0.9606. Therefore, it can be concluded that the overall detection performance was highly reliable and consistant across the datasets used in this study.

Links and sources

• Official / arXiv page

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