基于网格分类与纵横向注意力的城市道路车道线检测方法

A Method for Detecting Edge Lines of Traveling Lanes of Urban Roads Based on Grid Classification and Vertical-horizontal Attention

车道线检测是汽车安全辅助驾驶系统的基础模块,在城市道路场景下车道线存在受碾压致特征缺失、车辆间相互遮挡以及光照环境复杂多变等问题,本文提出基于网格分类与纵横向注意力的车道线检测方法。提取道路图像的全局特征图,将其划分为若干网格,计算网格中车道线的存在概率;通过将车道线检测转化为网格位置的分类,定位每条车道线的特征点;构建基于Ghost模块的主干网络,结合车道线的形状特征,引入纵横向注意力机制,通过增强车道线纹理特征和融合位置信息,获取缺失的细节特征;利用三次多项式,拟合车道线特征点,修正车道线的检测结果。基于TuSimple与CULane数据集,在ResNet18、ResNet34和DarkNet53中嵌入纵横向注意力模块,并开展对比实验。结果表明:在TuSimple数据集上,嵌入纵横向注意力模块后,模型精度均提升了约0.1%,与其他模型相比,Ghost-VHA模型的准确率为95.96%。在CULane数据集上,嵌入纵横向注意力模块可提升精度约0.65%,与其他模型相比,Ghost-VHA的F1分数为72.84%,提升了0.54%。在TuSimple与CULane数据集上,Ghost-VHA处理尺寸为288 px×800 px的图像仅需4.5 ms,具有良好的准确率和实时性。在CULane数据集上,网格列数量为300时效果最好,在TuSimple数据集上,网格列数量为50时效果最好。

Detecting edge lines of traveling lanes is fundamental to assisted vehicle safety-assisted driving systems.Due to the lane lines often exhibit missing features due to obstructions from vehicles and the complexities of the lighting conditions under various urban settings,a method for detecting edge lines of traveling lanes of urban roads based on grid classification and vertical-horizontal attention is proposed.The global feature maps are extracted from the road image and divided into multiple grids.Subsequently,the probability of the presence of edge lines of travelling lanes within each grid is calculated.By transforming the task of lane line detection into the grid position classification,the feature points associated with each lane line are accurately identified.The Ghost module is employed as the backbone.Additionally,vertical-horizontal attention(VHA)is introduced,enhancing lane line texture features,incorporating location information,and recovering missing details.The detection results are rectified by fitting the lane line feature points using cubic polynomials.The vertical-horizontal attention modules are embedded in ResNet18,ResNet34,and DarkNet53 to evaluate the proposed approach.The TuSimple and CULane datasets are utilized for conducting comparison experiments.Study results show that based on the TuSimple dataset,embed-ding the VHA module improves the accuracy by about 0.1%.Compared with other models,the accuracy of proposed Ghost-VHA is 95.96%.On the CULane dataset,embedding the VHA improves the accuracy by about 0.65%,and the corresponding F1 score of Ghost-VHA is 72.84%,which is 0.54%higher than other models.Analysis of the results across nine urban scenarios reveals that the"ground sign interference"scenario exhibits the highest F1 score,reaching 85.7%.Furthermore,the Ghost-VHA method demonstrates excellent real-time performance by processing a 288 px×800 px image within a mere 4.5 ms based on the TuSimple and CULane datasets while maintaining satisfactory accuracy.Based on the CULane dataset,this model works best when the number of grid columns is 300 and based on the TuSimple dataset,this model works best when the number of grid columns is 50.