Wang Qi, Han Tao, Qin Zequn, Gao Junyu, Li Xuelong
IEEE Trans Neural Netw Learn Syst. 2022 Mar;33(3):1066-1078. doi: 10.1109/TNNLS.2020.3039675. Epub 2022 Feb 28.
Many CNN-based segmentation methods have been applied in lane marking detection recently and gain excellent success for a strong ability in modeling semantic information. Although the accuracy of lane line prediction is getting better and better, lane markings' localization ability is relatively weak, especially when the lane marking point is remote. Traditional lane detection methods usually utilize highly specialized handcrafted features and carefully designed postprocessing to detect the lanes. However, these methods are based on strong assumptions and, thus, are prone to scalability. In this work, we propose a novel multitask method that: 1) integrates the ability to model semantic information of CNN and the strong localization ability provided by handcrafted features and 2) predicts the position of vanishing line. A novel lane fitting method based on vanishing line prediction is also proposed for sharp curves and nonflat road in this article. By integrating segmentation, specialized handcrafted features, and fitting, the accuracy of location and the convergence speed of networks are improved. Extensive experimental results on four-lane marking detection data sets show that our method achieves state-of-the-art performance.
最近，许多基于卷积神经网络（CNN）的分割方法已应用于车道线检测，并因其在语义信息建模方面的强大能力而取得了优异的成果。尽管车道线预测的准确性越来越高，但车道标记的定位能力相对较弱，尤其是当车道标记点较远时。传统的车道检测方法通常利用高度专业化的手工特征和精心设计的后处理来检测车道。然而，这些方法基于很强的假设，因此容易受到扩展性的影响。在这项工作中，我们提出了一种新颖的多任务方法，该方法：1）整合了CNN建模语义信息的能力和手工特征提供的强大定位能力；2）预测消失线的位置。本文还针对急转弯和不平坦道路提出了一种基于消失线预测的新颖车道拟合方法。通过整合分割、专业化手工特征和拟合，提高了定位精度和网络的收敛速度。在四车道标记检测数据集上的大量实验结果表明，我们的方法达到了当前的最优性能。
