# Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor

arXiv: https://arxiv.org/abs/2008.06204 · HTML: https://mlsanity.com/paper/2008.06204/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Event Camera Data Pre-training](https://mlsanity.com/paper/2301.01928/) (2023)
- [Event Camera Data Dense Pre-training](https://mlsanity.com/paper/2311.11533/) (2023)
- [Data-efficient Event Camera Pre-training via Disentangled Masked Modeling](https://mlsanity.com/paper/2403.00416/) (2024)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras](https://mlsanity.com/paper/2411.15276/) (2024)
- [Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network](https://mlsanity.com/paper/2309.13302/) (2023)
- [Computer vision-based model for detecting turning lane features on Florida's public roadways](https://mlsanity.com/paper/2406.08822/) (2024)
- [EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning](https://mlsanity.com/paper/2409.11813/) (2024)
- [Spiking Layer-Adaptive Magnitude-based Pruning](https://mlsanity.com/paper/2603.14946/) (2026)

## Abstract (arXiv)

Lane marker extraction is a basic yet necessary task for autonomous driving. Although past years have witnessed major advances in lane marker extraction with deep learning models, they all aim at ordinary RGB images generated by frame-based cameras, which limits their performance in extreme cases, like huge illumination change. To tackle this problem, we introduce Dynamic Vision Sensor (DVS), a type of event-based sensor to lane marker extraction task and build a high-resolution DVS dataset for lane marker extraction. We collect the raw event data and generate 5,424 DVS images with a resolution of 1280×800 pixels, the highest one among all DVS datasets available now. All images are annotated with multi-class semantic segmentation format. We then propose a structure-aware network for lane marker extraction in DVS images. It can capture directional information comprehensively with multidirectional slice convolution. We evaluate our proposed network with other state-of-the-art lane marker extraction models on this dataset. Experimental results demonstrate that our method outperforms other competitors. The dataset is made publicly available, including the raw event data, accumulated images and labels.