针对智能车辆在复杂场景下的车道线检测准确性问题，该文提出了一种融合多尺度空间注意力机制和路径聚合网络（PANet）的车道线检测算法。该算法首先引入行锚框UFLD车道线检测模型，并结合深度可分离卷积的特征金字塔增强模块PANet，以实现图像的多尺度特征提取；接着，网络框架中设计多尺度空间注意力模块，且引入SimAM轻量级注意力机制，以增强对目标特征的聚焦能力；然后，设计自适应特征融合模块，通过智能调整不同尺度特征图的融合权重，对PANet输出的特征图进行跨尺度融合，以提升网络对复杂特征的提取能力。在TuSimple数据集上的实验结果表明，所提算法的检测精度为96.84%，较原算法提升了1.02个百分点，优于传统的主流算法；在CULane数据集上的实验结果表明，所提算法的F₁值为72.74%，优于传统的主流算法，较原算法提升了4.34个百分点，尤其在强光和阴影等极端场景下的检测性能提升显著，说明所提算法在复杂场景下具有优异的检测能力；实时性测试结果显示，所提算法的推理速度达118.0 f/s，满足智能车辆的实时性需求。

Aiming at the problem of lane detection accuracy of intelligent vehicles in complex scenes, this paper proposed a lane line detection algorithm which incorporates a multi-scale spatial attention mechanism and a path aggregation network (PANet). The algorithm first introduced the pre-anchored frame UFLD lane detection model and incorporated a feature pyramid enhancement module PANet with depthwise separable convolution to achieve multi-scale feature extraction of images. Next, a multi-scale spatial attention module was designed in the network framework and a SimAM lightweight attention mechanism was introduced to enhance the focusing ability on target features. Then, an adaptive feature fusion module was designed to perform cross-scale fusion of feature maps output from PANet by intelligently adjusting the fusion weights of feature maps at different scales, so as to effectively enhance the network’s ability to extract complex features. Finally, the application of TuSimple dataset detection proves that the proposed algorithm achieves a detection accuracy of 96.84%, representing a 1.02 percentage point improvement over the original algorithm, and outperforms conventional mainstream algorithms. Experimental results on the CULane dataset demonstrate that the proposed algorithm achieves an F₁ score of 72.74%, outperfor-ming conventional mainstream methods with a 4.34 percentage point improvement over the baseline. Notably, it exhibits significant performance gains in extreme scenarios (e.g., strong illumination and shadows), confirming its superior detection capability in complex environments. In addition, the real-time test shows that the model infe-rence speed reaches 118 f/s, which meets the real-time demand of intelligent vehicles.