中文

Journal of Applied Sciences >

2026 , Vol. 44 >Issue 4: 598 - 614

DOI: https://doi.org/10.3969/j.issn.0255-8297.2026.04.006

Intelligent Visual Perception

Methods and Applications of Small Object Detection in Remote Sensing Images

  • WANG Yufan ,
  • SHAO Zilong ,
  • PU Yuanxue ,
  • ZHANG Haowen ,
  • ZHANG Jingyi ,
  • QIN Kun
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  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China

Received date: 2026-01-15

  Online published: 2026-08-01

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Abstract

Small object detection in remote sensing imagery is one of the core issues in the fields of remote sensing and computer vision, playing a crucial role in the intelligent understanding of remote sensing scenes and intelligent interpretation of remote sensing images. Due to factors such as the low pixel ratio of targets and complex scenes, small object detection in remote sensing imagery faces multiple challenges, including difficulties in target feature extraction, susceptibility to noise and occlusion interference, stringent requirements for bounding box regression accuracy, scarcity of dedicated datasets, and insufficient model design adaptability. The technical difficulties and bottlenecks of small object detection in remote sensing imagery were systematically reviewed. Six mainstream detection methods, including traditional remote sensing image processing detection methods, dataset augmentation-based detection methods, resolution enhancement-based detection methods,feature fusion and attention mechanism-based detection methods, Transformer-based detection methods, and anchor-free technology-based detection methods, were analyzed, and the technical principles, core innovations, and applicable scenarios of each method were detailed. The characteristics, annotation specifications, and application value of remote sensing small object datasets were summarized, and the applications of remote sensing small object detection technology in ship and navigation mark detection, vehicle detection,and military target detection were analyzed. Finally, future development directions for remote sensing small object detection, including multimodal fusion, lightweight architecture,and few-shot learning, were prospected.

Key words： small object detection; deep learning; data augmentation; Transformer; object detection dataset

Cite this article

WANG Yufan , SHAO Zilong , PU Yuanxue , ZHANG Haowen , ZHANG Jingyi , QIN Kun . Methods and Applications of Small Object Detection in Remote Sensing Images[J]. Journal of Applied Sciences, 2026 , 44(4) : 598 -614 . DOI: 10.3969/j.issn.0255-8297.2026.04.006

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