Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors focus on making lane detection in driving safer by improving how the system recognizes lane lines, which are often thin and can be hidden. They enhanced the feature extraction with a module that better captures lane structures and improved how the system scores and ranks lane guesses to avoid mistakes. Their method builds on an existing network and shows better accuracy on multiple lane detection datasets without slowing down the process. Overall, their approach helps reduce wrong detections and missed lanes during driving.
lane detectionanchor-based detectorsbackbone featuresnon-maximum suppression (NMS)token interactionsclassification confidencedynamic anchor scoringintersection-over-union (IoU)F1 scoreVIL-100 dataset
Authors
Weize Cai, Yongqi Dong, Zhida Shao, Yichen Liu, Zixin Fu
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.