Omni-Refinement Attention Network for Lane Detection

TL;DR

This paper introduces ORANet, a new lane detection system that improves performance in challenging driving conditions like shadows and curved roads.

Contribution

ORANet introduces two novel attention modules, EnCA and CSSA, which enhance both global and local feature processing for lane detection.

Findings

ORANet outperforms CLRNet in complex roadway scenarios with improved stability.

Under shadow conditions, ORANet achieves a nearly 3% higher F1 score than CLRNet.

Abstract

Lane detection is a fundamental component of perception systems in autonomous driving. Despite significant progress in this area, existing methods still face challenges in complex scenarios such as abnormal weather, occlusions, and curved roads. These situations typically demand the integration of both the global semantic context and local visual features to predict the lane position and shape. This paper presents ORANet, an enhanced lane detection framework built upon the baseline CLRNet. ORANet incorporates two novel modules: Enhanced Coordinate Attention (EnCA) and Channel–Spatial Shuffle Attention (CSSA). EnCA models long-range lane structures while effectively capturing global semantic information, whereas CSSA strengthens the precise extraction of local features and provides optimized inputs for EnCA. These components operate in hierarchical synergy, collectively establishing a complete enhancement pathway from refined local feature extraction to efficient global feature fusion. The experimental results demonstrate that ORANet achieves greater performance stability than CLRNet in complex roadway scenarios. Notably, under shadow conditions, ORANet achieves an F1 score improvement of nearly 3% over CLRNet. These results highlight the potential of ORANet for reliable lane detection in real-world autonomous driving environments.

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