Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (15): 1-17.DOI: 10.3778/j.issn.1002-8331.2112-0176

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Research on Small Target Detection Based on Deep Learning

ZHANG Yan, ZHANG Minglu, LYU Xiaoling, GUO Ce, JIANG Zhihong   

  1. 1.School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China  
    2.School of Mechatronic Engineering, Beijing Institute of Technology, Beijing 100081, China
  • Online:2022-08-01 Published:2022-08-01



  1. 1.河北工业大学 机械工程学院,天津 300401
    2.北京理工大学 机电工程学院,北京 100081

Abstract: The task of target detection is to quickly and accurately identify and locate predefined categories of objects from an image. With the development of deep learning techniques, detection algorithms have achieved good results for large and medium targets in the industry. The performance of small target detection algorithms based on deep learning still needs further improvement and optimization due to the characteristics of small targets in images such as small size, incomplete features and large gap between them and the background. Small target detection has a wide demand in many fields such as autonomous driving, medical diagnosis and UAV navigation, so the research has high application value. Based on extensive literature research, this paper firstly defines small target detection and finds the current difficulties in small target detection. It analyzes the current research status from six research directions based on these difficulties and summarizes the advantages and disadvantages of each algorithm. It makes reasonable predictions and outlooks on the future research directions in this field by combining the literature and the development status to provide a certain basic reference for subsequent research. This paper makes a reasonable prediction and outlook on the future research direction in this field, combining the literature and the development status to provide some basic reference for subsequent research.

Key words: target detection, small target, deep learning

摘要: 目标检测的主要目的是在图像中快速精准地识别定位出预定义类别的目标。而随着深度学习技术的不断发展,检测算法在相应行业大、中目标已达到了不错的成效。鉴于小目标在图像中尺寸较小、特征不全、与图像中背景差异大等特点,基于深度学习的小目标检测算法性能仍需要进一步提升和优化;小目标检测在无人驾驶、医疗诊断、无人机导航等多个领域都有着广泛的需求,因此研究有着很高的应用价值。在文献调研的基础上,先给出小目标检测定义,找到当前小目标检测的重难点;根据这些重难点从六个研究方向分析当前研究现状,并总结各算法优缺点;结合文献及发展现状对该领域未来的研究方向做出合理预测与展望,为后续研究提供一定基础参考。

关键词: 目标检测, 小目标, 深度学习