Zhou Xiaofeng, Zhang Peng
School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel). 2024 May 29;24(11):3502. doi: 10.3390/s24113502.
Effective lane detection technology plays an important role in the current autonomous driving system. Although deep learning models, with their intricate network designs, have proven highly capable of detecting lanes, there persist key areas requiring attention. Firstly, the symmetry inherent in visuals captured by forward-facing automotive cameras is an underexploited resource. Secondly, the vast potential of position information remains untapped, which can undermine detection precision. In response to these challenges, we propose FF-HPINet, a novel approach for lane detection. We introduce the Flipped Feature Extraction module, which models pixel pairwise relationships between the flipped feature and the original feature. This module allows us to capture symmetrical features and obtain high-level semantic feature maps from different receptive fields. Additionally, we design the Hierarchical Position Information Extraction module to meticulously mine the position information of the lanes, vastly improving target identification accuracy. Furthermore, the Deformable Context Extraction module is proposed to distill vital foreground elements and contextual nuances from the surrounding environment, yielding focused and contextually apt feature representations. Our approach achieves excellent performance with the F1 score of 97.00% on the TuSimple dataset and 76.84% on the CULane dataset.
有效的车道检测技术在当前的自动驾驶系统中起着重要作用。尽管深度学习模型凭借其复杂的网络设计已被证明具有很高的车道检测能力，但仍存在一些关键问题需要关注。首先，前置汽车摄像头捕捉的视觉图像中固有的对称性是一种未被充分利用的资源。其次，位置信息的巨大潜力仍未得到挖掘，这可能会影响检测精度。针对这些挑战，我们提出了FF-HPINet，一种新颖的车道检测方法。我们引入了翻转特征提取模块，该模块对翻转特征与原始特征之间的像素成对关系进行建模。该模块使我们能够捕捉对称特征，并从不同的感受野中获得高级语义特征图。此外，我们设计了分层位置信息提取模块，以精心挖掘车道的位置信息，极大地提高目标识别精度。此外，还提出了可变形上下文提取模块，以从周围环境中提炼出重要的前景元素和上下文细微差别，从而产生聚焦且上下文合适的特征表示。我们的方法在TuSimple数据集上的F1分数为97.00%，在CULane数据集上的F1分数为76.84%，取得了优异的性能。
