Open AccessA review of research on object detection based on deep learning
Author(s) -
Jun Deng,
Xiaojing Xuan,
Weifeng Wang,
Li Zhao,
Hanwen Yao,
Zhiqiang Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012028
Subject(s) - computer science , object detection , artificial intelligence , field (mathematics) , stage (stratigraphy) , machine learning , pattern recognition (psychology) , computer vision , mathematics , paleontology , pure mathematics , biology
As one of the important tasks in computer vision, target detection has become an important research hotspot in the past 20 years and has been widely used. It aims to quickly and accurately identify and locate a large number of objects of predefined categories in a given image. According to the model training method, the algorithms can be divided into two types: single-stage detection algorithm and two-stage detection algorithm. In this paper, the representative algorithms of each stage are introduced in detail. Then the public and special datasets commonly used in target detection are introduced, and various representative algorithms are analyzed and compared in this field. Finally, the potential challenges for target detection are prospected.