[Re] CLRNet: Cross Layer Refinement Network for Lane Detection
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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
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 2 Oct 2023.
Builds on
7 of 15 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2019Learning Lightweight Lane Detection CNNs by Self Attention Distillationcited by 162
- 2020Keep your Eyes on the Lane: Real-time Attention-guided Lane Detectioncited by 137
- 2022CLRNet: Cross Layer Refinement Network for Lane Detectioncited by 82
- 2020Attention-guided Context Feature Pyramid Network for Object Detectioncited by 8
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