@inproceedings{2c1bf36cfae6409a85c020947cf22966,
title = "Encoder-Decoder based Segmentation Model for UAV Street Scene Images",
abstract = "Global contextual information needs to be modeled precisely for accurate segmentation of images taken by Unmanned Aerial Vehicles (UAVs). This paper presents a transformer-based method for UAV street scene semantic segmentation. The method uses an encoder-decoder-based architecture to capture local and global context information in UAV images. Experimental result of the proposed method shows competitive performance against state-of-the-art methods by achieving mIoU of 61.93% on UAVid dataset.",
keywords = "Semantic segmentation, UAV street scene images, self-attention, transformer",
author = "Satyawant Kumar and Abhishek Kumar and Hong, {Hye Seong} and Lee, {Dong Gyu}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Consumer Electronics, ICCE 2023 ; Conference date: 06-01-2023 Through 08-01-2023",
year = "2023",
doi = "10.1109/ICCE56470.2023.10043528",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference on Consumer Electronics, ICCE 2023",
address = "United States",
}