# Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer

arXiv: https://arxiv.org/abs/2002.01177 · HTML: https://mlsanity.com/paper/2002.01177/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Focus on Local: Detecting Lane Marker from Bottom Up via Key Point](https://mlsanity.com/paper/2105.13680/) (2021)
- [LaneAF: Robust Multi-Lane Detection with Affinity Fields](https://mlsanity.com/paper/2103.12040/) (2021)
- [Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification](https://mlsanity.com/paper/2206.07389/) (2022)
- [HLA-Face: Joint High-Low Adaptation for Low Light Face Detection](https://mlsanity.com/paper/2104.01984/) (2021)
- [Robust Lane Detection via Expanded Self Attention](https://mlsanity.com/paper/2102.07037/) (2021)
- [Lane Detection with Versatile AtrousFormer and Local Semantic Guidance](https://mlsanity.com/paper/2203.04067/) (2022)
- [A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection](https://mlsanity.com/paper/2110.04079/) (2021)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection](https://mlsanity.com/paper/2409.17158/) (2024)
- [RONELDv2: A faster, improved lane tracking method](https://mlsanity.com/paper/2202.13137/) (2022)
- [Repainting and Imitating Learning for Lane Detection](https://mlsanity.com/paper/2210.05097/) (2022)
- [IBURD: Image Blending for Underwater Robotic Detection](https://mlsanity.com/paper/2502.17706/) (2025)
- [Efficient Sequential Neural Network with Spatial-Temporal Attention and Linear LSTM for Robust Lane Detection Using Multi-Frame Images](https://mlsanity.com/paper/2602.03669/) (2026)
- [Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks](https://mlsanity.com/paper/2204.01795/) (2022)

## Abstract (arXiv)

Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day. Although multi-task learning and contextual-information-based methods have been proposed to solve the problem, they either require additional manual annotations or introduce extra inference overhead respectively. In this paper, we propose a style-transfer-based data enhancement method, which uses Generative Adversarial Networks (GANs) to generate images in low-light conditions, that increases the environmental adaptability of the lane detector. Our solution consists of three parts: the proposed SIM-CycleGAN, light conditions style transfer and lane detection network. It does not require additional manual annotations nor extra inference overhead. We validated our methods on the lane detection benchmark CULane using ERFNet. Empirically, lane detection model trained using our method demonstrated adaptability in low-light conditions and robustness in complex scenarios. Our code for this paper will be publicly available.