文档介绍：48 2 2022 2
第 卷 第 期 WANG Changjian，DING Yong，LU Pancheng
210016
（College of Automation Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing ，China）
【Abstract】The existing target detection usually suffers from insufficient sample number and different image angles，resulting
in low detection accuracy. This paper proposes a target detection algorithm using Faster R-CNN by combining improved
Feature Pyramid Network（FPN）structure with a relation on the traditional FPN structure，the bottom-up feature
fusion process is added to extract rich semantic information and location information of the feature map of the
location information and shape features between candidate regions are used to construct relation features，which are
subsequently fused with deep features，so fully extract the overall information of the feature map and realize target detection.
The experimental results on PASCAL VOC 2007 and NWPU VHR-10 datasets show that compared with the FPN+Faster
R-CNN algorithm，the proposed algorithm increases the Intersection over Union（IoU）by about 10 percentage points and
detection accuracy by about percenta