Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pre-train the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the-art, delivering the best testing accuracy (98.38%), precision (0.937), and F1-measure (0.924) on the normal scene testing set, together with the best overall accuracy (98.36%) and precision (0.844) in the challenging scene test set, while the training time can be substantially shortened.

翻译：车道线检测对车辆定位至关重要，因此成为自动驾驶及众多智能先进驾驶辅助系统的基础。现有基于视觉的车道线检测方法未能充分利用有价值的特征并聚合上下文信息，特别是连续帧图像中车道线与其他区域间的相互关联。为填补这一研究空白并提升车道线检测性能，本文提出了一种结合掩码序列自编码器自预训练与定制PolyLoss微调的端到端神经网络模型流水线，该模型采用多连续图像帧作为输入。掩码序列自编码器通过以重建随机掩码图像中的缺失像素为目标，对神经网络模型进行预训练。随后，在车道线检测分割的微调阶段，将连续图像帧作为输入，利用反向传播机制迁移预训练模型权重并进行更新，同时采用定制PolyLoss计算输出车道线检测结果与标注真值之间的加权误差。大量实验结果表明，采用所提流水线后，车道线检测模型在正常场景与挑战性场景下的性能均可超越当前最优水平：在正常场景测试集上获得最佳测试准确率（98.38%）、精确率（0.937）和F1分数（0.924），在挑战性场景测试集上获得最佳总体准确率（98.36%）和精确率（0.844），同时训练时间得以大幅缩短。