Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (6): 1243-1259.DOI: 10.3778/j.issn.1673-9418.2112035

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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.


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
  • 通讯作者: + E-mail:
  • 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。


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



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

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