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RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

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成果类型:
期刊论文
作者:
Zhang, Pan;Deng, Hongwei;Chen, Zhong
通讯作者:
Deng, HW
作者机构:
[Zhang, Pan; Deng, Hongwei; Chen, Zhong] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
通讯机构:
[Deng, HW ] H
Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
语种:
英文
关键词:
Attention mechanism;RT-YOLO;small target detection;YOLOv5s
期刊:
计算机、材料和连续体(英文)
ISSN:
1546-2218
年:
2023
卷:
75
期:
1
页码:
1411-1430
基金类别:
Funding Statement: This work was supported in part by the Scientific Research Project of Hunan Provincial Department of Education under Grant 18A332 and 19A066, authors HW.D and Z.C, http://kxjsc.gov.hnedu.cn/; in part by the Science and Technology Plan Project of Hunan Province under Grant 2016TP1020, author HW.D, http://kjt.hunan.gov.cn/.
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
摘要:
In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is ad...

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