基于有效感受野的区域推荐网络

doi:10.16451/j.cnki.issn1003-6059.202005002

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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.

Key words： Deep Convolutional Network Object Detection Region Proposal Effective Receptive Field Region Proposal Network(RPN)

Received: 29 November 2019

ZTFLH:

TP 391.41

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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	ZHANG Shengyu
	DONG Shifeng
	JIAO Lin
	WANG Qijin
	WANG Hongqiang

Cite this article:

ZHANG Shengyu,DONG Shifeng,JIAO Lin等. Region Proposal Network Based on Effective Receptive Field[J]. , 2020, 33(5): 393-400.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202005002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I5/393

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