计算机科学
领域(数学分析)
目标检测
特征(语言学)
对象(语法)
人工智能
计算机视觉
模式识别(心理学)
数学
语言学
数学分析
哲学
作者
Songtao Tang,Leiming Zhang,Xudong Liu,Rongfu Lv,Ruyi Qin
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2025-01-01
卷期号:13: 121686-121703
被引量:5
标识
DOI:10.1109/access.2025.3586649
摘要
To address UAV-based detection challenges including scale variation, high target density, and hardware limitations, we propose RFHS-RTDETR with four innovations: (1) RMConv, a reparameterized lightweight module for efficient multi-scale feature extraction; (2) FSConv fusing Scharr operator and Fourier transform to enhance edge preservation and robustness; (3) AH module combining HiLo attention for dense target recognition; (4) SOPS Feature Pyramid with hierarchical feature integration of P2 features and dynamic upsampling for small objects. On VisDrone2019, RFHS-RTDETR reduces FLOPs and parameters by 16.2% and 34.3% versus RT-DETR-R18, while improving precision by 2.2%, mAP50 by 2%, and FPS by 17.1%.These advancements demonstrate its practicality for resource-constrained aerial scenarios.
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