A semantic segmentation method for UAV image based on improved BiseNetv2

Fengjuan Guo; Sen Han; Jiahang Zhao; Jiale Han; Jing Xin

doi:10.1117/12.3009294

16 October 2023 A semantic segmentation method for UAV image based on improved BiseNetv2

Fengjuan Guo, Sen Han, Jiahang Zhao, Jiale Han, Jing Xin

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 1280331 (2023) https://doi.org/10.1117/12.3009294
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for “Human”, the IoU of T-BiseNetv2 is about 4.3% higher than that of the popular UNetFormer.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fengjuan Guo, Sen Han, Jiahang Zhao, Jiale Han, and Jing Xin "A semantic segmentation method for UAV image based on improved BiseNetv2", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 1280331 (16 October 2023); https://doi.org/10.1117/12.3009294

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KEYWORDS

Image segmentation

Semantics

Unmanned aerial vehicles

Transformers

Feature fusion

Image fusion

RGB color model

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