计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (9): 1933-1953.DOI: 10.3778/j.issn.1673-9418.2203070

• 综述·探索 • 上一篇    下一篇

深度学习应用于目标检测中失衡问题研究综述

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
  • 收稿日期:2022-03-02 修回日期:2022-04-28 出版日期:2022-09-01 发布日期:2022-09-15
  • 通讯作者: + E-mail: Fuyan@hrbeu.edu.cn
  • 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。
    付岩(1978—),女,黑龙江哈尔滨人,硕士,讲师,CCF会员,主要研究方向为人工智能、编译技术等。
    吴艳霞(1979—),女,黑龙江哈尔滨人,博士,副教授,CCF会员,主要研究方向为计算机体系结构、编译技术等。
    梁鹏举(1995—),男,黑龙江哈尔滨人,硕士研究生,主要研究方向为编译技术、软件工程。
    韩希(1963—),男,黑龙江哈尔滨人,研究员,主要研究方向为信息化、数据处理。
  • 基金资助:
    中央高校基本科研业务费专项资金(3072021CFT0602)

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)

摘要:

目前手工提取特征进行目标检测的方案被深度学习所取代,深度学习技术极大地推动了目标检测技术的发展。目标检测也成为了深度学习最重要的应用领域之一。目标检测是同时预测给定图像中对象实例的类别和位置,这项技术已经广泛应用于医学影像、遥感技术、监控安防、自动驾驶等领域。但是随着深度学习技术的应用领域的多元化,目标检测中出现的失衡问题成为了目前优化目标检测训练模型的一个新的切入点。主要分析在运用机器学习技术解决目标检测问题过程中,模型在每个训练阶段会出现的四类失衡问题:数据失衡、尺度失衡、相对空间失衡以及分类与回归失衡。剖析问题产生的主要原因,研究具有代表性的经典解决方案,阐述目标检测在各个领域中存在的问题。通过对目标检测失衡问题的分析和总结,讨论未来目标检测失衡问题的研究方向。

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

Abstract:

The current scheme of manually extracting features for object detection has been replaced by deep learning.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

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