VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection

TL;DR

This paper introduces VIL-100, a comprehensive video lane detection dataset, and proposes MMA-Net, a novel model that leverages memory aggregation to improve lane detection in videos, outperforming existing methods.

Contribution

The paper presents a new large-scale video lane detection dataset and a baseline model utilizing multi-level memory aggregation for improved detection accuracy.

Findings

MMA-Net outperforms state-of-the-art lane detection methods.

VIL-100 dataset provides high-quality annotations for video lane detection.

The approach effectively leverages temporal information for better detection.

Abstract

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.