华中科技大学学报（自然科学版）2025，Vol.53Issue(3)：117-126,10.DOI:10.13245/j.hust.250465
基于轻量化Transformer的车道线检测方法
Lane line detection method based on lightweight transformer
摘要
Abstract
When deploying autonomous driving and advanced driver assistance systems on mobile devices,excessive network parameters resulted in large storage space occupation and high deployment threshold of hardware systems,affecting the popularization of autonomous driving and assisted driving technologies.To address these issues,a lane detection method based on lightweight transformer within the framework of a semantic segmentation network was proposed.In the encoder part,a MobileVIT network tailored for lightweight design of transformer modules was utilized to perceive global dependency relationships,capturing lane-related feature information at longer distances and reducing network parameter count.In the decoder part,a bilateral up-sampling decoder was employed to refine the segmentation results,yielding more accurate pixel-level segmentation results.Finally,a confidence evaluation network was used to determine the number of lane lines.Additionally,a self-attention distillation method was introduced during network training to enhance the attention on lane line areas without increasing network parameters.To meet various application requirements,three detection networks with different parameter counts were designed.Experimental results demonstrate that the parameter counts of the three designed networks are 26.03%,13.19%,and 7.52%of the typical lane detection network SCNN-ResNet34,respectively.The accuracies are improved by 0.46%,0.15%,and 0.09%respectively,achieving high detection accuracy with fewer parameters,making it convenient for deployment on mobile devices.关键词
交通工程/语义分割/车道线检测/MobileViT网络/自注意力蒸馏Key words
traffic engineering/semantic segmentation/lane line detection/MobileVIT network/self-attention distillation分类
交通工程引用本文复制引用
陈广秋,刘枫铭,段锦,黄丹丹..基于轻量化Transformer的车道线检测方法[J].华中科技大学学报（自然科学版）,2025,53(3):117-126,10.基金项目
国家自然科学基金资助项目(62127813). （62127813）
