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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 10

Survey on deep learning-based small object detection algorithms

Zhang Qin1,2
Guo Wei'an2
1. College of Information Engineering, Fuzhou Polytechnic, Fuzhou 350108, China
2. Sino-German College of Applied Sciences, Tongji University, Shanghai 201804, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0067

Abstract

Small object detection is an important branch of object detection, playing a critical role in applications such as intelligent surveillance, autonomous driving, medical image analysis, and remote sensing. However, due to the small pixel proportion of targets, weak feature representation, complex backgrounds, and the trade-off between detection accuracy and speed, significant technical challenges remain. Based on an extensive literature review, this study outlines the technical challenges and solutions for small object detection, analyzing core issues such as insufficient feature representation, inadequate utilization of contextual information, and sample imbalance. Key advances, including multi-scale feature fusion, attention mechanisms, and knowledge distillation, are summarized. Using MS COCO and TinyPerson datasets, the detection efficiency and accuracy of mainstream algorithms are compared, highlighting the strengths and weaknesses of different methods. Furthermore, future research directions, such as generative feature learning, self-supervised learning, and dynamic architecture design, are explored to provide insights for the further development of small object detection technologies.

Foundation Support

国家自然科学基金资助项目(62273263,72171172,71771176,92367101)
上海市自然科学基金资助项目(23ZR1465400)
福建省中青年教师教育科研项目(JAT220652)
福州职业技术学院校级科研计划项目(FZYKJJHZD202401)
福州职业技术学院引导计划项目(FZYKJZXYD202201)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0067
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 10

Publish History

[2025-06-17] Accepted Paper

Cite This Article

张琴, 郭为安. 深度学习小目标检测算法综述 [J]. 计算机应用研究, 2025, 42 (10). (2025-06-19). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0067. (Zhang Qin, Guo Wei'an. Survey on deep learning-based small object detection algorithms [J]. Application Research of Computers, 2025, 42 (10). (2025-06-19). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0067. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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