Multiscale Efficient Channel Attention for Fusion Lane Line Segmentation. (7th December 2021)

• Record Type: Journal Article
• Authors: Liu, Kang; Gao, Xin
• Other Names: Zeng Chao Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-12-07
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/6791882
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The use of multimodal sensors for lane line segmentation has become a growing trend. To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to adaptively calculate the contribution of the fused feature channels. We verified the effect of multimodal fusion on the KITTI benchmark dataset and A2D2 dataset and proved the effectiveness of the proposed method on the enhanced KITTI dataset. Our method achieves robust lane line segmentation, which is 4.53% higher than the direct fusion on the precision index, and obtains the highest F2 score of 79.72%. We believe that our method introduces an optimization idea of modal data structure level for multimodal fusion.