返回
Road surface semantic segmentation for autonomous driving
DOI:10.7717/peerj-cs.2250.png)
摘要
En 中文
Although semantic segmentation is widely employed in autonomous driving, its performance in segmenting road surfaces falls short in complex traffic environments. This study proposes a frequency-based semantic segmentation with a transformer (FSSFormer) based on the sensitivity of semantic segmentation to frequency information. Specifically, we propose a weight-sharing factorized attention to select important frequency features that can improve the segmentation performance of overlapping targets. Moreover, to address boundary information loss, we used a cross-attention method combining spatial and frequency features to obtain further detailed pixel information. To improve the segmentation accuracy in complex road scenarios, we adopted a parallel-gated feedforward network segmentation method to encode the position information. Extensive experiments demonstrate that the mIoU of FSSFormer increased by 2% compared with existing segmentation methods on the Cityscapes dataset.
Keyword:
Semantic segmentation
Transformer
Weight-sharing factorized attention
Cross-attention combining spatial and frequency features
Parallel-gated feedforward network
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
3.4K
被引数:
6.9K
机构
引用论文
TRIM13 inhibited cell proliferation and induced cell apoptosis by regulating NF-κB pathway in non-small-cell lung carcinoma cells
Gene
IF0
Health Literacy – a review of research using the European Health Literacy Questionnaire (HLS-EU-Q16) in 2010-2018健康素养-2010-2018使用欧洲健康素养问卷 (HLS-EU-Q16) 进行的研究综述
Oxygen Diffusion Phenomena and Hydrogen Incorporation in Reducing BaTiO3Ceramics Doped with Ho below Solubility LimitOxygen扩散现象及低于溶解度极限掺杂Ho的还原性BaTiO3陶瓷中的氢固溶

