# [Re] CLRNet: Cross Layer Refinement Network for Lane Detection

arXiv: https://arxiv.org/abs/2310.01142 · HTML: https://mlsanity.com/paper/2310.01142/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [Attention-guided Context Feature Pyramid Network for Object Detection](https://mlsanity.com/paper/2005.11475/) (2020)

## Abstract (arXiv)

The following work is a reproducibility report for CLRNet: Cross Layer Refinement Network for Lane Detection. The basic code was made available by the author. The paper proposes a novel Cross Layer Refinement Network to utilize both high and low level features for lane detection. The authors assert that the proposed technique sets the new state-of-the-art on three lane-detection benchmarks