![Figure 1 for [Re] CLRNet: Cross Layer Refinement Network for Lane Detection](/_next/image?url=https%3A%2F%2Ffigures.semanticscholar.org%2F2e0230997e71dec815de9168e6124a7ec586ef61%2F6-Figure1-1.png&w=640&q=75)
![Figure 2 for [Re] CLRNet: Cross Layer Refinement Network for Lane Detection](/_next/image?url=https%3A%2F%2Ffigures.semanticscholar.org%2F2e0230997e71dec815de9168e6124a7ec586ef61%2F6-Table1-1.png&w=640&q=75)
![Figure 3 for [Re] CLRNet: Cross Layer Refinement Network for Lane Detection](/_next/image?url=https%3A%2F%2Ffigures.semanticscholar.org%2F2e0230997e71dec815de9168e6124a7ec586ef61%2F7-Table2-1.png&w=640&q=75)
![Figure 4 for [Re] CLRNet: Cross Layer Refinement Network for Lane Detection](/_next/image?url=https%3A%2F%2Ffigures.semanticscholar.org%2F2e0230997e71dec815de9168e6124a7ec586ef61%2F11-Figure2-1.png&w=640&q=75)
Abstract: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