Region Proposal Network Based on Effective Receptive Field
ZHANG Shengyu1,2, DONG Shifeng2, JIAO Lin2, WANG Qijin2, WANG Hongqiang2
1. Institutes of Physical Science and Information Technology, Anhui University, Hefei 230039; 2. Special Robot Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
Abstract Object detection methods based on convolutional neural network(CNN) optimize region proposal to achieve a higher detection accuracy. Therefore, an effective receptive field(eRF) based region proposal network is proposed. A sample matching method based on eRF is introduced into regional proposal network. Thus, the intersection over union(IoU) based sample matching rule is improved. The representation ability of feature information in the region proposal generation stage is enhanced. The number of region proposal and anchor boxes is greatly reduced. The parameter settings of anchor boxes are also simplified. The detection accuracy on Pascal VOC datasets is improved in combination with Fast R-CNN detector. The effectiveness of proposed method is verified.
Fund:Supported by National Natural Science Foundation of China(No.61773360,61973295), Key Research and Development Project of Anhui Province(No.201904a07020092)
About author:: (ZHANG Shengyu, master student. His research interests include pattern recognition and object detection.);(DONG Shifeng, master student. His research interests include pattern recognition and object detection.);(JIAO Lin, Ph.D candidate. Her research interests include pattern recognition and deep learning.);(WANG Qijin, Ph.D candidate. His research interests include object detection and network security.);(WANG Hongqiang(Corresponding author), Ph.D. professor. His research inte-rests include pattern recognition, deep learning and big data analysis.)
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2020, Vol. 33 
