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Yun Ren [1] and Changren Zhu [1] and Shunping Xiao [2]

Academic Editor: Francesco Marotti de Sciarra

1, ATR National Lab, National University of Defense Technology, Changsha 410073, China, nudt.edu.cn

2, State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China, nudt.edu.cn

Received Oct 23, 2017; Accepted Dec 12, 2017

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.

1. Introduction

Modern object detectors [1, 2] always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. These two parts are thought to be mutually independent in the traditional object detectors while they can be considered to be a unified course in the modern object detectors. The feature extractor in traditional object detection methods is usually a hand-engineered descriptor, such as SIFT [3] and HOG [4]. At the same time, the feature classifier is usually a linear SVM [5], a nonlinear boosted classifier [6], or an additive kernel SVM [7]. However, the deep ConvNets have dominated the feature extractor of the modern object detectors in various application scenarios [8–11]. Aside from being capable of representing higher-level semantics, ConvNets are also more robust to variance in scale and thus facilitate recognition from features computed on a single input scale.

The successful RCNN [12] method applies high-capacity convolutional neural networks to extract a fixed-length feature vector from each region which is fed to a set of class-specific linear SVMs. It firstly pretrains the network by supervision for image classification with abundant data and then fine-tunes the network for detection where data is scarce. In fact, it only can be considered a hybrid of traditional detectors and deep ConvNets. Although its feature extractor is replaced by pretrained deep ConvNets, the classifier still uses a traditional model which is a set of class-specific linear SVMs. SPPnet [13] is also a hybrid model using convolutional layers to extract full-image features followed by a set of class-specific binary linear SVMs like RCNN. What is different is that the spatial pyramid pooling layer proposed by SPPnet enables feature extraction in arbitrary windows from...

  

 
 
 
 
   
   
   
         
       
       
       
   
       
       

 

 

 

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