@inproceedings{a17dba3fb0014adbb6ed163204bebd32,
title = "Research on semantic segmentation of UAV images based on deep learning",
abstract = "UAV technology has developed rapidly in recent years, Images extracted by UAV are widely used in urban division, crop classification, land monitoring etc. However, there are problems in UAV image segmentation such as image category imbalance, object scale variation, and insufficient utilization of contextual information, etc. To address the above problems, this paper uses optimized deeplabv3+ network model, and cross-entropy loss function for balancing the dataset samples in the experimental process for image semantic segmentation research. The results show that the algorithm of this paper has a high accuracy rate for semantic segmentation of UAV images, and can recognize each category of UAV images better, and the segmentation effect is better.",
keywords = "UAV images, deep learning, deeplabv3+, semantic segmentation",
author = "Lihua He and Xinyan Cao and Yuheng Wang and Liye Ren",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2nd International Conference on Digital Signal and Computer Communications, DSCC 2022 ; Conference date: 08-04-2022 Through 10-04-2022",
year = "2022",
doi = "10.1117/12.2641404",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Sandeep Saxena",
booktitle = "Second International Conference on Digital Signal and Computer Communications, DSCC 2022",
address = "United States",
}