基于3D-ResNet和PINet的轻量级时空高速公路车道检测方法
Lightweight Spatiotemporal Highway Lane Detection via 3D-ResNet and PINet with ROI-Aware Attention
AI总结：
本文提出了一种轻量级端到端高速公路车道检测架构，结合时空信息提升实际驾驶场景的鲁棒性，通过3D卷积神经网络和实例分割，提出两种模型整合3D-ResNet编码器和PINet解码器，改进多尺度特征表示和ROI区域聚焦。
摘要：
本文提出了一种轻量级端到端高速公路车道检测架构，结合时空信息提升实际驾驶场景的鲁棒性，通过3D卷积神经网络和实例分割，提出两种模型整合3D-ResNet编码器和PINet解码器，改进多尺度特征表示和ROI区域聚焦。实验在TuSimple数据集（高速公路驾驶场景）上进行，结果显示所提第二种模型达到93.40%的准确率，显著减少假阴性。与现有2D和3D基线相比，本方法在参数更少和延迟更低的情况下实现了改进性能。该架构已通过离线训练和实时推理在City, St George's University of London的自主系统实验室中验证。这些结果表明，所提模型适合集成到高级驾驶辅助系统（ADAS）中，具有向全车道辅助系统（LAS）扩展的潜力。
英文摘要：
This paper presents a lightweight, end-to-end highway lane detection architecture that jointly captures spatial and temporal information for robust performance in real-world driving scenarios. Building on the strengths of 3D convolutional neural networks and instance segmentation, we propose two models that integrate a 3D-ResNet encoder with a Point Instance Network (PINet) decoder. The first model enhances multi-scale feature representation using a Feature Pyramid Network (FPN) and Self-Attention mechanism to refine spatial dependencies. The second model introduces a Region of Interest (ROI) detection head to selectively focus on lane-relevant regions, thereby improving precision and reducing computational complexity. Experiments conducted on the TuSimple dataset (highway driving scenarios) demonstrate that the proposed second model achieves 93.40% accuracy while significantly reducing false negatives. Compared to existing 2D and 3D baselines, our approach achieves improved performance with fewer parameters and reduced latency. The architecture has been validated through offline training and real-time inference in the Autonomous Systems Laboratory at City, St George's University of London. These results suggest that the proposed models are well-suited for integration into Advanced Driver Assistance Systems (ADAS), with potential scalability toward full Lane Assist Systems (LAS).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
