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

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 travel-ling lanes within each grid is calculated. By transforming the task of lane line detection into the grid position classifi-cation, 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 re-sults 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 satis-factory 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.