sis 18: e32

Research Article

Region proposal network based on context information feature fusion for vehicle detection

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  • @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: Online First},
        volume={},
        number={},
        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
Zengyong Xu1,*
  • 1: School of Automotive Studies, Henan College of Transportation, Zhengzhou 451100 China
*Contact email: zxcvfdsa5024@foxmail.com

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This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173795.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.