Object Detection Based on FastFaster RCNN Employing Fully Convolutional Architectures
Joint Authors
Ren, Yun
Zhu, Changren
Xiao, Shunping
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors.
The deeper and wider convolutional architectures are adopted as the feature extractor at present.
However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier.
In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets) of various depths for feature classification, especially using the fully convolutional architectures.
In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN.
Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.
American Psychological Association (APA)
Ren, Yun& Zhu, Changren& Xiao, Shunping. 2018. Object Detection Based on FastFaster RCNN Employing Fully Convolutional Architectures. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1206923
Modern Language Association (MLA)
Ren, Yun…[et al.]. Object Detection Based on FastFaster RCNN Employing Fully Convolutional Architectures. Mathematical Problems in Engineering No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1206923
American Medical Association (AMA)
Ren, Yun& Zhu, Changren& Xiao, Shunping. Object Detection Based on FastFaster RCNN Employing Fully Convolutional Architectures. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1206923
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206923