Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (9): 1933-1953.DOI: 10.3778/j.issn.1673-9418.2203070

• Surveys and Frontiers • Previous Articles     Next Articles

Review of Research on Imbalance Problem in Deep Learning Applied to Object Detection

REN Ning1, FU Yan1,+(), WU Yanxia1, LIANG Pengju1, HAN Xi2   

  1. 1. Harbin Engineering University, Harbin 150001, China
    2. Heilongjiang Province Natural Resources Technology Guarantee Center, Harbin 150030, China
  • Received:2022-03-02 Revised:2022-04-28 Online:2022-09-01 Published:2022-09-15
  • About author:REN Ning, born in 1996, Ph.D. candidate. Her research interests include deep learning object detection and image processing.
    FU Yan, born in 1978, M.S., lecturer, member of CCF. Her research interests include artificial intelligence and compilation technology, etc.
    WU Yanxia, born in 1979, Ph.D., associate professor, member of CCF. Her research interests include computer architecture, compilation technology, etc.
    LIANG Pengju, born in 1995, M.S. candidate. His research interests include compilation technology and software engineering.
    HAN Xi, born in 1963, researcher. His research interests include informatization and data processing.
  • Supported by:
    Fundamental Research Funds for the Central Universities of China(3072021CFT0602)


任宁1, 付岩1,+(), 吴艳霞1, 梁鹏举1, 韩希2   

  1. 1.哈尔滨工程大学,哈尔滨 150001
    2.黑龙江省自然资源技术保障中心,哈尔滨 150030
  • 通讯作者: + E-mail:
  • 作者简介:任宁(1996—),女,河南濮阳人,博士研究生,主要研究方向为深度学习目标检测、图像处理。
  • 基金资助:


The current scheme of manually extracting features for object detection has been replaced by deep learning. Deep learning technology has greatly promoted the development of object detection technology. Object detection has also become one of the most important application fields of deep learning. Object detection is to simultaneously predict the category and position of object instances in a given image. This technology has been widely used in medical imaging, remote sensing technology, monitoring and security, automatic driving and other fields. However, with the diversification of object detection application fields, the imbalance problem in the application of deep learning to object detection has become a new entry point to optimize the object detection training model. This paper mainly analyzes the use of machine learning technology to solve the object detection problem. There are four kinds of imbalance problems in each training stage of the model: data imbalance, scale imbalance, relative space imbalance and classification and regression imbalance. This paper analyzes the main reasons for the problem, studies representative classical solutions, and expounds the problems existing in object detection in various fields. By analyzing and summarizing the object detection imbalance problems, this paper discusses the directions of the imbalance of object detection in the future.

Key words: deep learning, object detection, data imbalance, scale imbalance, relative spatial imbalance, classifi-cation and regression imbalance



关键词: 深度学习, 目标检测, 数据失衡, 尺度失衡, 相对空间失衡, 分类与回归失衡

CLC Number: