陕西师范大学学报（自然科学版）2026，Vol.54Issue(5)：43-51,9.DOI:10.15983/j.cnki.jsnu.2026506
基于B-RESA的车道线检测模型
Lane detection model based on B-RESA
摘要
Abstract
Lane detection is a fundamental task in autonomous driving and intelligent transportation systems,requiring both high accuracy and real-time performance.Despite the widespread adoption of deep learning techniques,achieving an optimal trade-off between detection accuracy and inference efficiency remains a significant challenge.A novel lane detection framework based on the bidirectional recurrent feature-shift aggregator(B-RESA)is proposed,inspired by the RESA architecture.Specifically,B-RESA performs bidirectional recurrent feature shifting along both vertical and horizontal axes of the feature maps,enabling the network to capture long-range spatial dependencies more effectively.To further improve feature aggregation,a directional-aware weighting(DW)module that adaptively fuses features from different directions based on learned importance weights is introduced,thereby enhancing the model's robustness in complex road conditions.Extensive experiments on the CULane and TuSimple datasets demonstrate that the proposed method achieves superior performance in both detection accuracy and inference speed compared to existing approaches.Ablation studies confirm that the DW module contributes significantly to performance gains,and that B-RESA notably accelerates inference without sacrificing accuracy.关键词
自动驾驶/车道线检测/双向循环移位/动态特征融合Key words
autonomous driving/lane detection/bidirectional recurrent shift/dynamic feature fusion分类
信息技术与安全科学引用本文复制引用
陈亚瑞,张健伟,牛淑静,史艳翠..基于B-RESA的车道线检测模型[J].陕西师范大学学报（自然科学版）,2026,54(5):43-51,9.基金项目
国家自然科学基金(61976156) （61976156）
