Drone-Based High-Precision Object Detection in Remote Sensing with Attention-Guided Feature FusionOA
Small object detection in remote sensing imagery is a challenging task due to the small size of targets,complex background,and low contrast,which makes achieving high precision difficult.To enhance the accuracy of detection,this study proposes a novel oriented object detection model with three significant innovations:Firstly,a lightweight feature extraction network is designed to achieve efficient feature representation at a reduced computational cost,which is particularly effective for the recognition of small targets in remote sensing imagery.Secondly,a Feature-Focused Channel Attention(FFCA)is introduced that enhances the model’s ability to focus on small target areas by combining spatial and channel attention,enhancing the model’s capacity to capture and represent features more effectively.Lastly,an attention-guided multi-scale feature fusion module is proposed to integrate features from different levels,which substantially boosts the model’s ability to accurately detect small-scale objects,especially in remote sensing scenarios with vast fields of view and complex backgrounds.The experimental outcomes validate that our model achieves the best detection performance on two benchmark public datasets for remote sensing imagery,confirming its effectiveness and practicality in remote small object detection tasks.
Hanxiang Wang;Yanfen Li;Yuanke Zhang;Junliang Shang;Guangshun Li;Liem Dinh-Tien;L.Minh Dang;Hyoung-Kyu Song;Hyeonjoon Moon
School of Computer Science,Qufu Normal University,Rizhao 276826,ChinaSchool of Computer Science,Qufu Normal University,Rizhao 276826,ChinaSchool of Computer Science,Qufu Normal University,Rizhao 276826,ChinaSchool of Computer Science,Qufu Normal University,Rizhao 276826,ChinaSchool of Computer Science,Qufu Normal University,Rizhao 276826,ChinaFaculty of Fundamental Sciences,Van Lang University,Ho Chi Minh City 70000,VietnamInstitute of Research and Development,Duy Tan University,Da Nang 550000,Vietnam Department of Information and Communication Engineering,and Convergence Engineering for Intelligent Drone,Sejong University,Seoul 05006,Republic of Korea Faculty of Information Technology,Duy Tan University,Da Nang 550000,VietnamDepartment of Information and Communication Engineering,and Convergence Engineering for Intelligent Drone,Sejong University,Seoul 05006,Republic of KoreaDepartment of Computer Science and Engineering,Sejong University,Seoul 05006,Republic of Korea
航空航天
remote sensingoriented object detectionattention mechanismsmall object recognition
《Tsinghua Science and Technology》 2026 (2)
P.1263-1281,19
supported by the Young Scientists Fund of the National Natural Science Foundation of China(No.62502271)the Young Scientists Fund of the Natural Science Foundation of Shandong Province(No.ZR2025QC630)the Natural Science Foundation of Rizhao City(Nos.RZ2024ZR33 and RZ2024ZR34)the Key Research and Development Program of Shandong Province(No.2025CXGC010113)the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2020R1A6A1A03038540).
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