Open AccessOverview of two-stage object detection algorithms
Author(s) -
Lixuan Du,
Rongyu Zhang,
Xiaotian 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 - 10.1088/1742-6596/1544/1/012033
Subject(s) - stage (stratigraphy) , computer science , object (grammar) , object detection , field (mathematics) , artificial intelligence , algorithm , pattern recognition (psychology) , mathematics , paleontology , pure mathematics , biology
Nowadays, object detection has gradually become a quite popular field. From the traditional methods to the methods used at this stage, object detection technology has made great progress, and is still continuously developing and innovating. This paper reviews two-stage object detection algorithms used at this stage, explaining in detail the working principles of Faster R-CNN, R-FCN, FPN, and Casecade R-CNN and analyzing the similarities and differences between these four two-stage object detection algorithms. Then we used HSRC2016 ship dataset to perform experiments with Faster R-CNN, R-FCN, FPN, and Casecade R-CNN and compared the effectiveness of them with experimental results.