Lane detection plays a key role in autonomous driving. While car cameras always take streaming videos on the way, current lane detection works mainly focus on individual images (frames) by ignoring dynamics along the video. In this work, we collect a new video instance lane detection (VIL-100) dataset, which contains 100 videos with in total 10,000 frames, acquired from different real traffic scenarios. All the frames in each video are manually annotated to a high-quality instance-level lane annotation, and a set of frame-level and video-level metrics are included for quantitative performance evaluation. Moreover, we propose a new baseline model, named multi-level memory aggregation network (MMA-Net), for video instance lane detection. In our approach, the representation of current frame is enhanced by attentively aggregating both local and global memory features from other frames. Experiments on the new collected dataset show that the proposed MMA-Net outperforms state-of-the-art lane detection methods and video object segmentation methods. We release our dataset and code at https://github.com/yujun0-0/MMA-Net.

本研究收集了100个样本，通过对收录的视频进行筛选得到了10000个视频中的标注，提出了名为MMA-Net的新基线模型，通过集成多个逐帧预测，并通过注意聚合得到的局部和全局特征来增强当前预测。实验表明，MMA-Net可以优于现有的车道检测和视频对象分割方法。

VIL-100: 视频实例车道检测新数据集与基础模型