# Towards Lightweight Lane Detection by Optimizing Spatial Embedding

arXiv: https://arxiv.org/abs/2008.08311 · HTML: https://mlsanity.com/paper/2008.08311/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [A Keypoint-based Global Association Network for Lane Detection](https://mlsanity.com/paper/2204.07335/) (2022)
- [Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving](https://mlsanity.com/paper/2203.00858/) (2022)
- [Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints](https://mlsanity.com/paper/2112.15351/) (2021)
- [Robust Lane Detection via Expanded Self Attention](https://mlsanity.com/paper/2102.07037/) (2021)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [Model-Agnostic Defense for Lane Detection against Adversarial Attack](https://mlsanity.com/paper/2103.00663/) (2021)

## Abstract (arXiv)

A number of lane detection methods depend on a proposal-free instance segmentation because of its adaptability to flexible object shape, occlusion, and real-time application. This paper addresses the problem that pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize. A translation invariance of convolution, which is one of the supposed strengths, causes challenges in optimizing pixel embedding. In this work, we propose a lane detection method based on proposal-free instance segmentation, directly optimizing spatial embedding of pixels using image coordinate. Our proposed method allows the post-processing step for center localization and optimizes clustering in an end-to-end manner. The proposed method enables real-time lane detection through the simplicity of post-processing and the adoption of a lightweight backbone. Our proposed method demonstrates competitive performance on public lane detection datasets.