Aircraft detection algorithm of remote sensing image based on adaptive feature fusion and multi-scale output

LI Zhong; BAI Rui-jun; HONG Jun; LI Ya-lun; WANG Gao; YANG Jian

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Aircraft detection algorithm of remote sensing image based on adaptive feature fusion and multi-scale output

更新时间：2026-09-04

- Aircraft detection algorithm of remote sensing image based on adaptive feature fusion and multi-scale output
- Vol. 38, Issue 4, Pages: 40-45(2021)
- 作者机构：
  
  1. 中北大学机构软件学院,山西,太原,030051
  2. 山西省信息产业技术研究院有限公司,山西,太原,030051
  3. 中北大学信息与通信工程学院,山西,太原,030051
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
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李众, 白瑞君, 洪军, 李亚伦, 王高, 杨剑. 基于自适应特征融合及多尺度输出的遥感图像飞机检测算法[J]. 微电子学与计算机, 2021, 38(4): 40-45, 51.

LI Zhong, BAI Rui-jun, HONG Jun, et al. Aircraft detection algorithm of remote sensing image based on adaptive feature fusion and multi-scale output[J]. 2021, 38(4): 40-45.
李众, 白瑞君, 洪军, 李亚伦, 王高, 杨剑. 基于自适应特征融合及多尺度输出的遥感图像飞机检测算法[J]. 微电子学与计算机, 2021, 38(4): 40-45, 51. DOI：

LI Zhong, BAI Rui-jun, HONG Jun, et al. Aircraft detection algorithm of remote sensing image based on adaptive feature fusion and multi-scale output[J]. 2021, 38(4): 40-45. DOI：

摘要

针对当前YOLOv3算法在遥感图像中复杂场景飞机漏检、误检等问题，提出一种基于自适应特征融合及多尺度输出的遥感图像飞机检测算法.该算法首先使用K-means++代替K-means算法对数据集进行聚类，解决了K-means初始聚类中心不稳定性；然后，在YOLOv3网络基础上增加了一个含有分辨率信息的尺度，更有利于检测小目标飞机；最后，在网络模型四尺度输出前增加了自适应特征融合层，解决了不同尺度的特征融合不充分以及减少或消除反向传导受到负样本的影响.实验结果表明，改进的YOLOv3算法在遥感图像上飞机检测精度达到96.17%，比YOLOv3算法精度提高了2.6%.

Abstract

In view of the problems such as missing detection and false detection of complex scene aircraft in remote sensing image by the YOLOv3 algorithm

an aircraft detection algorithm based on adaptive feature fusion and multiscale output is proposed. Firstly

K-means ++ is used to cluster the data set instead of K-means algorithm

which solves the instability of the initial clustering center of k-means. Then

on the basis of YOLOv3 network

a scale with resolution information is added

which is more conducive to the detection of small target aircraft. Finally

an adaptive feature fusion layer is added before the four scale output of the network model

which solves the problem of insufficient feature fusion at different scales and reduces or eliminates the influence of negative samples on reverse conduction. The experimental results show that the detection accuracy of the improved algorithm reaches 96.17% in the remote sensing image

which is 2.6% higher than that of the algorithm.

关键词

Keywords

references

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