Main > Laser & Optoelectronics Progress >  Volume 57 >  Issue 20 >  Page 201505 > Article
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  • Received: Dec. 10, 2019

    Accepted: Feb. 25, 2020

    Posted: Oct. 1, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Yang Fengbao (yfengb@163.com)

    DOI: 10.3788/LOP57.201505

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    Yingjie Liu, Fengbao Yang, Peng Hu. Parallel FPN Algorithm Based on Cascade R-CNN for Object Detection from UAV Aerial Images[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201505

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Laser & Optoelectronics Progress, Vol. 57, Issue 20, 201505 (2020)

Parallel FPN Algorithm Based on Cascade R-CNN for Object Detection from UAV Aerial Images

Liu Yingjie, Yang Fengbao*, and Hu Peng

Author Affiliations

  • School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi 030051, China

Abstract

The detection and recognition of small targets are always difficult for researchers in the field of target detection, resulting in the feature extracted from the model not having good expression ability, so the detection result of small targets is poor. This paper presents a modified algorithm based on feature pyramid network(FPN). Specifically, the parallel branch is devised on the original basis to fuse the feature information of two different up-sampling methods to enhance the representation ability of small objects. Meanwhile, a multiple threshold detector named Cascade R-CNN is added to prompt the localization ability of small objects. Experiments are conducted on UAV aerial image datasets. The experimental results reveal that the average precision of the proposed algorithm under MS COCO dataset increases by 9.7 percentage compared to that of the initial FPN algorithm; hence, the proposed algorithm has a good detection performance.

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