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

Authors

  • Zengyong Xu Henan College of Transportation

DOI:

https://doi.org/10.4108/eai.27-1-2022.173161

Keywords:

RPN, vehicle detection, context information fusion

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This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173795.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

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Published

27-01-2022

How to Cite

1.
Xu Z. Region proposal network based on context information feature fusion for vehicle detection. EAI Endorsed Scal Inf Syst [Internet]. 2022 Jan. 27 [cited 2024 Nov. 21];9(4):e15. Available from: https://publications.eai.eu/index.php/sis/article/view/334