Research Article
Region proposal network based on context information feature fusion for vehicle detection
@ARTICLE{10.4108/eai.27-1-2022.173161, author={Zengyong Xu}, title={Region proposal network based on context information feature fusion for vehicle detection}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={SIS}, year={2022}, month={1}, keywords={RPN, vehicle detection, context information fusion}, doi={10.4108/eai.27-1-2022.173161} }- Zengyong Xu
Year: 2022
Region proposal network based on context information feature fusion for vehicle detection
SIS
EAI
DOI: 10.4108/eai.27-1-2022.173161
Abstract
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173795.
By using the traditional methods, the feature information extracted from vehicle target detection is insufficient, which leads to the low accuracy in identifying small target vehicles or blocked targets. Therefore, we propose a region proposal network (RPN) based on context information feature fusion for vehicle detection. RPN obtains feature vectors of fixed length as vehicle target features. Context information fusion network obtains the corresponding context information features on the feature maps of different layers. Finally, the two features are fused. In addition, in order to solve the problem of data imbalance, experiments on PASCAL VOC2007 and PASCAL VOC2012 data sets with difficult sample training show that the proposed method has significantly improved the mean average accuracy (mAP) compared with other methods.
Copyright © 2022 Zengyong Xu et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
