Tian Wei, Yu Xianwang, Hu Haohao
Tongji University, Shanghai 201804, China.
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany.
Sensors (Basel). 2023 Jul 20;23(14):6545. doi: 10.3390/s23146545.
Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks. In this paper, we establish a novel multi-task encoder-decoder framework for the simultaneous detection of lanes and lane markings. This approach deploys a dual-branch architecture to extract image information from different scales. By revealing the spatial constraints between lanes and lane markings, we propose an interactive attention learning for their feature information, which involves a Deformable Feature Fusion module for feature encoding, a Cross-Context module as information decoder, a Cross-IoU loss and a Focal-style loss weighting for robust training. Without bells and whistles, our method achieves state-of-the-art results on tasks of lane marking detection (with 32.53% on IoU, 81.61% on accuracy) and lane segmentation (with 91.72% on mIoU) of the BDD100K dataset, which showcases an improvement of 6.33% on IoU, 11.11% on accuracy in lane marking detection and 0.22% on mIoU in lane detection compared to the previous methods.
基于视觉的道路车道区域和车道标记识别是智能驾驶车辆不可或缺的功能，尤其对于定位、地图构建和规划任务而言。然而，由于交通场景日益复杂，如遮挡和不连续性，从单目相机捕获的图像中检测车道和车道标记一直具有挑战性。车道和车道标记具有很强的位置相关性，并且在驾驶场景中受到空间几何关系的约束。大多数现有研究仅探索单一任务，即要么是车道标记检测，要么是车道检测，并未考虑两者之间的内在联系，也未利用这种关系的建模来提高两项任务的检测性能。在本文中，我们建立了一种新颖的多任务编码器 - 解码器框架，用于同时检测车道和车道标记。该方法采用双分支架构从不同尺度提取图像信息。通过揭示车道和车道标记之间的空间约束，我们针对它们的特征信息提出了一种交互式注意力学习方法，其中包括用于特征编码的可变形特征融合模块、作为信息解码器的跨上下文模块、交叉交并比损失和用于稳健训练的焦点式损失加权。我们的方法在没有花里胡哨的技巧的情况下，在BDD100K数据集的车道标记检测任务（交并比为32.53%，准确率为81.61%）和车道分割任务（平均交并比为91.72%）上取得了领先成果，与先前方法相比，在车道标记检测的交并比上提高了6.33%，准确率提高了11.11%，在车道检测的平均交并比上提高了0.22%。
