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Overview of two-stage object detection algorithms

Journal of Physics Conference Series · 2020 · Vol. 1544(1) · pp. 012033–012033
Lixuan DuRongyu ZhangXiaotian Wang

Abstract

Abstract 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.

Advanced Neural Network ApplicationsMaritime Navigation and SafetyAnomaly Detection Techniques and ApplicationsStage (stratigraphy)Computer scienceObject (grammar)Object detectionField (mathematics)Artificial intelligenceAlgorithmPattern recognition (psychology)Mathematics
Citations
198
FWCI
7.56
field-weighted impact
References
10
Percentile
98%
vs. same field & year
Citations per year
References
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2016 · 52,930 citations
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