融合坐标注意力机制的车道线检测算法研究

Research on lane line detection algorithm integrating coordinate attention

车道线检测是智能驾驶中的一项关键技术,快速准确地检测出车道线位置对提升驾驶车辆的安全具有重要的意义。因此,提出改进的行方向位置分类车道线检测方法,在特征提取主干网络中融合了坐标注意力机制,增强特征图中的有效位置的权重,其次引入ELAN模块、MP下采样模块,提升模型特征提取的能力;在推理的时候利用结构重参数化的思想,将卷积和BN层融合,加快推理速度。为了验证改进模型的性能,将改进的模型在TuSimple和CULane两大经典车道线数据集上进行实验验证,检测的精度与原模型相比分别提升了0.09%和2.5%,验证了模型改进的有效性。

Lane detection is a key technology in intelligent driving.It is of great significance to detect lane position quickly and accurately to improve the safety of driving vehicles.Therefore,an improved lane detection method based on row direction position classification is proposed.The coordinate attention mechanism is integrated into the feature extraction backbone network to enhance the weight of effective positions in the feature map.Secondly,ELAN module and MP subsampling module are introduced to improve the feature extraction capability of the model.In reasoning,the idea of structure re-parameterization is used to fuse convolution and BN layer to speed up reasoning.In order to verify the performance of the improved model,the improved model was tested on TuSimple and CULane two classical lane data sets,and the detection accuracy was increased by 0.09%and 2.5%respectively compared with the original model,which verified the effectiveness of the improved model.