End-to-End Lane Detection: A Two-Branch Instance Segmentation Approach

Engineering notes

Experimental results demonstrate that the proposed method achieves F1-scores of 76.0% and 96.9% on the CULane and Tusimple datasets, respectively, significantly enhancing the accuracy and reliability of lane detection.

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

To address the challenges of lane line recognition failure and insufficient segmentation accuracy in complex autonomous driving scenarios, this paper proposes a dual-branch instance segmentation method that integrates multi-scale modeling and dynamic feature enhancement. By constructing an encoder-decoder architecture and a cross-scale feature fusion network, the method effectively enhances the feature representation capability of multi-scale information through the integration of high-level feature maps (rich in semantic information) and low-level feature maps (retaining spatial localization details), thereby improving the prediction accuracy of lane line morphology and its variations. Additionally, hierarchical dilated convolutions (with dilation rates 1/2/4/8) are employed to achieve exponential expansion of the receptive field, enabling better fusion of multi-scale features. Experimental results demonstrate that the proposed method achieves F1-scores of 76.0% and 96.9% on the CULane and Tusimple datasets, respectively, significantly enhancing the accuracy and reliability of lane detection. This work provides a high-precision, real-time solution for autonomous driving perception in complex environments.

Links and sources

• Official / arXiv page

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