School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
Sensors (Basel). 2021 Oct 28;21(21):7156. doi: 10.3390/s21217156.
Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network's attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.
车道和路标的分割在自动驾驶中至关重要，在这个领域已经提出了许多相关的方法。然而，大多数方法都是基于单帧预测的，这导致了帧间结果的不稳定。一些语义多帧分割方法会产生误差积累，并且不够快。因此，我们提出了一种深度学习算法，该算法考虑了相邻图像帧的连续性信息，包括图像序列处理和端到端可训练的多输入单输出网络，以联合处理车道和路标的分割。为了在相邻帧中强调具有高概率的目标位置，并细化当前帧的分割结果，我们明确考虑了帧间的时间一致性，扩展了前一帧的分割区域，并使用相邻帧的光流来反转过去的预测，然后将其作为网络在训练和推理中的附加输入，从而提高网络对过去帧目标区域的关注。我们在百度 ApolloScape 路牌分割数据集和 CULane 数据集上对车道和路标的分割进行了实验，并为不同的网络提供了基准测试。实验结果表明，这种方法可以将视频车道和路标的分割速度提高 2.5 倍，准确率提高 1.4%，并且时间一致性最多只降低 2.2%。
