融合多尺度特征的残差车道线检测网络  

Residual lane detection algorithm based on multi-scale features

作　　者：蒋源 张欢 朱高峰 朱凤华[3] 熊刚[3] JIANG Yuan;ZHANG Huan;ZHU Gaofeng;ZHU Fenghua;XIONG Gang(Huzhou Vocational&Technical College,Huzhou 313000,China;Huzhou Key Laboratory of IoT Intelligent System Integration Technology,Huzhou 313000,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;School of Rail Transit,Shandong Jiaotong University,Jinan 250300,China)

机构地区：[1]湖州职业技术学院,浙江湖州313000 [2]湖州市物联网智能系统集成技术重点实验室,浙江湖州313000 [3]中国科学院自动化研究所,北京100190 [4]山东交通学院轨道交通学院,山东济南250300

出　　处：《测绘通报》2024年第10期71-76,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(U1909204)。

摘　　要：针对车道线分布范围广、占像素少、特征不易提取的问题,本文构建了一种基于多尺度特征融合的残差车道线检测网络。首先,以残差双边网络为基础,采用双边特征聚合模块,利用语义分支的上下文信息指导同一阶段的细节分支的特征响应,并融合两分支的信息;然后,针对不同阶段具有不同尺度,使用多尺度自适应特征对齐融合模块,构建采样前后偏移向量索引表,降低因简单采样而造成的细节信息缺失;最后,引入空间注意力机制,增强模型的长距离特征捕捉能力。试验结果表明,本文模型在3个公开数据集上均取得了良好效果,其中在CULane数据集上的准确度达77.89%,比目前主流算法高2%。To address the issues of wide lane line distribution range,sparse pixel coverage,and difficulty in feature extraction,this essay constructs a residual lane line detection network based on multi-scale feature fusion.This network is built upon a residual bilateral network and incorporates a bilateral feature aggregation module.It leverages the contextual information from the semantic branch to guide the feature responses of the detail branch within the same stage,thereby integrating information from both branches.Different stages operate at varying scales,and a multi-scale adaptive feature alignment fusion module is used to construct a sampling pre-and post-offset vector index table,reducing detail information loss caused by simple sampling.Additionally,a spatial attention mechanism is introduced to enhance the model's ability to capture long-distance features.Experimental results show that the proposed method performs well across three public datasets,achieving an accuracy of 77.89% on the CULane dataset,which is 2% higher than the current mainstream algorithms.

关 键 词：车道线检测 双边分割网络 多尺度 注意力机制 端到端 

分 类 号：P237[天文地球—摄影测量与遥感]