Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (8): 214-221.doi: 10.11896/jsjkx.241000019

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Improved RT-DETR Algorithm for Small Object Detection in Remote Sensing Images

SHEN Tao1, ZHANG Xiuzai1,2, XU Dai1   

  1. 1 School of Electronic and Information Engineering,Nanjing University of Information Science & Technology,Nanjing 210044,China
    2 Jiangsu Province Atmospheric Environment and Equipment Technology Collaborative,Nanjing University of Information Science & Technology,Nanjing 210044,China
  • Received:2024-10-08 Revised:2025-02-07 Online:2025-08-15 Published:2025-08-08
  • About author:SHEN Tao,born in 2000,postgraduate.His main research interests include deep learning and object detection.
    ZHANG Xiuzai,born in 1989,Ph.D.His main research interests include meteoro-logical communication technology and security,and machine learning.
  • Supported by:
    Comparative Study on Artificial Intelligence Strategies Between China and America of National Social Science Foundation of China(22BZZ080).

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Abstract: To address the high miss rate and false detection rate of target detection algorithms in remote sensing images and the poor performance in detecting small objects,this paper proposes an improved RT-DETR target detection algorithm.To enhance the model'scapability of detecting targets of different sizes in remote sensing images,variable kernel convolution and diversified branch structures are employed to enrich multi-scale representation capabilities.To avoid the loss of small object information due to downsampling,Haar wavelet downsampling is used to retain as much feature information as possible.To prevent the loss of small object feature information during complex network iterations and pooling,the SPABC3 module is designed to enhance high-contribution information and suppress redundant information through symmetric activation functions and residual connections.Experimental results show that the improved RT-DETR algorithm achieves mAP@0.5 of 42.7% and 95.3% on the VisDrone2019 dataset and RSOD dataset,outperforming other mainstream comparison algorithms and improving the detection accuracy of small objects in remote sensing images,thereby meeting the detection requirements for small objects in remote sensing images.

Key words: Small object detection, RT-DETR, AKConv, Haar wavelet downsampling, Swift parameter-free attention

CLC Number: 

  • TP393
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