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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 3598316, 7 pages
Research Article

Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures

1ATR National Lab, National University of Defense Technology, Changsha 410073, China
2State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

Correspondence should be addressed to Yun Ren

Received 23 October 2017; Accepted 12 December 2017; Published 9 January 2018

Academic Editor: Francesco Marotti de Sciarra

Copyright © 2018 Yun Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.