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

• URL: http://arxiv.org/abs/2608.09610v1
• Date: Mon, 10 Aug 2026 13:52:04 GMT
• Title: Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection
• Abstract summary: Lane detection requires recovering thin, elongated, and frequently occluded lane structures under challenging driving conditions.<n>We propose a structure-enhanced and quality-aware framework that improves lane representation and dynamic-anchor scoring.
• Score: 3.276955471607996
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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