@inproceedings{fce1e98bef1f42878f0a6470a898b2fd,
title = "FusionFormer: An Off-Road Sence Semantic Segmentation Network Based on Data Fusion and Hierarchical Transformer",
abstract = "The off-road environment poses significant challenges and obstacles to the further development of environmental perception due to the irregularity of its objects and the randomness of their distribution. In order to pursue higher precision of semantic segmentation in complex and unordered environments with irregular objects and uneven quantities, the FusionFormer is raised, which is based on image data fusion, hierarchical Transformer and Focal Loss. The network has strong learning capabilities by fusing depth and image information, using Transformer hierarchical to obtain multi-scale features, adopting Focal Loss to address class imbalance issues. The experiment corroborate that FusionFormer is Extremely capable to improve the precision and multi-class semantic segmentation capabilities in off-road scene semantic segmentation tasks.",
keywords = "Data fusion, Focal loss, Off-road scenes, Semantic segmentation, Transformer",
author = "Duan, {An Zhi} and Yue Ma and Wang, {Yun Feng}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 20th Chinese Intelligent Systems Conference, CISC 2024 ; Conference date: 26-10-2024 Through 27-10-2024",
year = "2024",
doi = "10.1007/978-981-97-8658-9_8",
language = "English",
isbn = "9789819786572",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "75--83",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Huihua Yang",
booktitle = "Proceedings of 2024 Chinese Intelligent Systems Conference",
address = "Germany",
}