An Enhanced Region Proposal Network for object detection using deep learning method

Faster Region-based Convolutional Network (Faster R-CNN) is a state-of-the-art object detection method. However, the object detection effect of Faster R-CNN is not good based on the Region Proposal Network (RPN). Inspired by RPN of Faster R-CNN, we propose a novel proposal generation method called E...

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Bibliographic Details
Published inPloS one Vol. 13; no. 9; p. e0203897
Main Authors Chen, Yu Peng, Li, Ying, Wang, Gang
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 20.09.2018
Public Library of Science (PLoS)
Subjects
Analysis
Artificial neural networks
Biology and Life Sciences
Computer and Information Sciences
Computer vision
Machine learning
Methods
Physical Sciences
Research and Analysis Methods
Social Sciences
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Summary:Faster Region-based Convolutional Network (Faster R-CNN) is a state-of-the-art object detection method. However, the object detection effect of Faster R-CNN is not good based on the Region Proposal Network (RPN). Inspired by RPN of Faster R-CNN, we propose a novel proposal generation method called Enhanced Region Proposal Network (ERPN). Four improvements are presented in ERPN. Firstly, our proposed deconvolutional feature pyramid network (DFPN) is introduced to improve the quality of region proposals. Secondly, novel anchor boxes are designed with interspersed scales and adaptive aspect ratios. Thereafter, the capability of object localization is increased. Thirdly, a particle swarm optimization (PSO) based support vector machine (SVM), termed PSO-SVM, is developed to distinguish the positive and negative anchor boxes. Fourthly, the classification part of multi-task loss function in RPN is improved. Consequently, the effect of classification loss is strengthened. In this study, our proposed ERPN is compared with five object detection methods on both PASCAL VOC and COCO data sets. For the VGG-16 model, our ERPN obtains 78.6% mAP on VOC 2007 data set, 74.4% mAP on VOC 2012 data set and 31.7% on COCO data set. The performance of ERPN is the best among the comparison object detection methods. Furthermore, the detection speed of ERPN is 5.8 fps. Additionally, ERPN obtains good effect on small object detection.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0203897