Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection

2026-08-10

AI Summary

This work addresses structural discontinuities in lane detection caused by occlusions and complex scenes, as well as the misalignment between classification confidence and localization quality that leads to false positives and missed detections. Without altering the inference pipeline of the Anchor Decomposition Network (ADNet), the authors propose two key enhancements: first, a Gated Horizontal-Vertical Token module is introduced to strengthen directional structural feature continuity in the backbone network; second, a Line-Quality-Aware dynamic anchor scoring mechanism is devised to recalibrate anchors based on quality supervision, hard negative suppression, and pairwise ranking—eliminating the need for additional network branches. Evaluated on VIL-100, the method improves ADNet-R34’s F1@50 from 89.97 to 91.28, while experiments on CULane and TuSimple further confirm its effectiveness and low computational overhead.

Abstract

Lane detection requires recovering thin, elongated, and frequently occluded lane structures under challenging driving conditions. While anchor-based detectors provide efficient candidate generation, their performance is limited by two coupled issues: backbone features often lose structural continuity along partially visible lanes, and classification confidence may decouple from line-level localization quality, allowing inaccurate anchors to persist before non-maximum suppression (NMS). We propose a structure-enhanced and quality-aware framework that improves lane representation and dynamic-anchor scoring while preserving the inference pipeline of the Anchor Decomposition Network (ADNet). Specifically, a Gated Horizontal-Vertical Token (GHVT) module enhances mid- and high-level backbone features via lightweight directional token interactions with a learnable residual gate. In parallel, Line-Quality-Aware Dynamic Anchor Scoring (LQAS) calibrates existing classification logits using quality supervision, hard-negative suppression, and pairwise ranking without adding inference branches. On the VIL-100 dataset, our method improves ADNet-R34 from 89.97 to 91.28 in F1 score at the 0.5 intersection-over-union threshold (F1@50), reducing both false positives and false negatives. Additional experiments on CULane and TuSimple datasets, extensive ablations, score-distribution diagnostics, and runtime analysis confirm complementary structural and ranking improvements with minimal computational overhead.

Problem

• lane detection
• structural continuity
• anchor scoring
• localization quality
• occluded lanes

Innovation

• structure-enhanced features
• quality-aware scoring
• dynamic anchor calibration
• gated token interaction
• lane detection