SRE-YOLOv8: : An Improved Uav Object Detection Model Utilizing Swin Transformer and RE-FPN

Authors

Language

English

Publication Date

6-17-2024

Journal

Sensors

DOI

10.3390/s24123918

PMID

38931702

PMCID

PMC11207483

PubMedCentral® Posted Date

June 2024

PubMedCentral® Full Text Version

Post-print

Abstract

To tackle the intricate challenges associated with the low detection accuracy of images taken by unmanned aerial vehicles (UAVs), arising from the diverse sizes and types of objects coupled with limited feature information, we present the SRE-YOLOv8 as an advanced method. Our method enhances the YOLOv8 object detection algorithm by leveraging the Swin Transformer and a lightweight residual feature pyramid network (RE-FPN) structure. Firstly, we introduce an optimized Swin Transformer module into the backbone network to preserve ample global contextual information during feature extraction and to extract a broader spectrum of features using self-attention mechanisms. Subsequently, we integrate a Residual Feature Augmentation (RFA) module and a lightweight attention mechanism named ECA, thereby transforming the original FPN structure to RE-FPN, intensifying the network's emphasis on critical features. Additionally, an SOD (small object detection) layer is incorporated to enhance the network's ability to recognize the spatial information of the model, thus augmenting accuracy in detecting small objects. Finally, we employ a Dynamic Head equipped with multiple attention mechanisms in the object detection head to enhance its performance in identifying low-resolution targets amidst complex backgrounds. Experimental evaluation conducted on the VisDrone2021 dataset reveals a significant advancement, showcasing an impressive 9.2% enhancement over the original YOLOv8 algorithm.

Keywords

deep learning, object detection, YOLOv8, Swin Transformer, feature pyramid network, computational perception

Published Open-Access

yes

Recommended Citation

, "SRE-YOLOv8: : An Improved Uav Object Detection Model Utilizing Swin Transformer and RE-FPN" (2024). Faculty, Staff and Student Publications. 368.
https://digitalcommons.library.tmc.edu/uthshis_docs/368