计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (6): 1243-1259.DOI: 10.3778/j.issn.1673-9418.2112035

• 综述·探索 • 上一篇    下一篇

深度学习应用于遮挡目标检测算法综述

孙方伟1, 李承阳1,2, 谢永强1,+(), 李忠博1, 杨才东1, 齐锦1   

  1. 1. 军事科学院 系统工程研究院,北京 100141
    2. 北京大学 信息科学技术学院,北京 100871
  • 收稿日期:2021-12-09 修回日期:2022-03-15 出版日期:2022-06-01 发布日期:2022-06-20
  • 通讯作者: + E-mail: xyq_ams@outlook.com
  • 作者简介:孙方伟(1996—),男,山东青岛人,硕士研究生,主要研究方向为目标检测、目标跟踪、语义分割。
    李承阳(1995—),男,辽宁鞍山人,博士研究生,主要研究方向为目标检测、数据挖掘、机器学习。
    谢永强(1972—),男,北京人,博士,研究员,主要研究方向为机器学习、多媒体技术、智能系统架构等。
    李忠博(1983—),男,北京人,博士,高级工程师,主要研究方向为多媒体技术、机器学习、云视频等。
    杨才东(1996—),男,贵州六盘水人,硕士研究生,主要研究方向为超分辨率重建。
    齐锦(1971—),女,北京人,硕士,高级工程师,主要研究方向为多媒体技术、智能系统架构、云计算等。

Review of Deep Learning Applied to Occluded Object Detection

SUN Fangwei1, LI Chengyang1,2, XIE Yongqiang1,+(), LI Zhongbo1, YANG Caidong1, QI Jin1   

  1. 1. Academy of Systems Engineering, Academy of Military Sciences, Beijing 100141, China
    2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
  • Received:2021-12-09 Revised:2022-03-15 Online:2022-06-01 Published:2022-06-20
  • About author:SUN Fangwei, born in 1996, M.S. candidate. His research interests include object detection, object tracking and semantic segmentation.
    LI Chengyang, born in 1995, Ph.D. candidate. His research interests include object detection, data mining and machine learning.
    XIE Yongqiang, born in 1972, Ph.D., researcher. His research interests include machine learning, multimedia technology, intelligent system architecture, etc.
    LI Zhongbo, born in 1983, Ph.D., senior engineer. His research interests include multimedia technology, machine learning, cloud video, etc.
    YANG Caidong, born in 1996, M.S. candidate. His research interest is super-resolution reconstruction.
    QI Jin, born in 1971, M.S., senior engineer. Her research interests include multimedia technology, intelligent system architecture, cloud computing, etc.

摘要:

遮挡目标检测长期以来是计算机视觉中的一个难点和研究热点。目前的深度学习基于卷积神经网络,将目标检测任务作为分类任务和回归任务来处理。当目标被遮挡时,遮挡物会混淆目标之间的特征,使得深度网络不能很好地识别和推理,降低检测器在理想场景下的性能。考虑到遮挡在现实中的普遍性,对遮挡目标的有效检测具有重要研究价值。为了进一步促进遮挡目标检测的发展,对基于深度学习的遮挡目标检测算法进行了全面总结,并对已有的遮挡检测算法进行归类、分析、比较。在对目标检测进行简单概述基础上,首先,对遮挡目标检测的相关背景、研究的难点以及遮挡数据集进行了介绍;然后,对遮挡检测优化算法主要按照目标结构、损失函数、非极大值抑制以及部分语义四方面进行归纳分析,在对各种算法之间的联系以及发展脉络进行阐述后,对各种算法性能进行了比较;最后,指出了遮挡目标检测仍面临的困难,并对遮挡目标检测未来的发展方向进行了展望。

关键词: 遮挡目标检测, 深度学习, 损失函数, 非极大值抑制, 部分语义

Abstract:

Occluded object detection has long been a difficulty and hot topic in the field of computer vision. Based on convolutional neural network, the deep learning takes the object detection task as a classification and regression task to handle, and obtains remarkable achievements. The mask confuses the features of object when the object is occluded, making the deep convolutional neural network cannot handle it well and reducing the performance of detector in ideal scenes. Considering the universality of occlusion in reality, the effective detection of occluded object has important research value. In order to further promote the development of occluded object detection, this paper makes a comprehensive summary of occluded object detection algorithms, and makes a reasonable classification and analysis. First of all, based on a simple overview of object detection, this paper introduces the relevant theoretic background, research difficulties and datasets about occluded object detection. After, this paper focuses on the algo-rithms to improve the performance of occluded object detection from the aspects of object structure, loss function, non-maximum suppression and semantic partial. This paper compares the performance of different detection algo-rithms after summarizing the relationship and development of various algorithms. Finally, this paper points out the difficulties of occluded object detection and looks forward to its future development directions.

Key words: occluded object detection, deep learning, loss function, non-maximum suppression, semantic partial

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