Remote Sensing Image Object Detection Based on Bidirectional Feature Fusion and Feature Selection

XIAO Jin-sheng; ZHANG Shu-hao; CHEN Yun-hua; WANG Yuan-fang; YANG Li-heng

doi:10.12263/DZXB.20210354

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Remote Sensing Image Object Detection Based on Bidirectional Feature Fusion and Feature Selection

PAPERS | 更新时间：2025-12-08

- Remote Sensing Image Object Detection Based on Bidirectional Feature Fusion and Feature Selection
- ACTA ELECTRONICA SINICA Vol. 50, Issue 2, Pages: 267-272(2022)
- 作者机构：
  
  1.武汉大学电子信息学院，湖北武汉 430072
  2.广东工业大学计算机学院，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210354
  CLC： TP183;TP751.1
- Received：14 March 2021，
  
  Revised：2022-01-06，
  
  Published：25 February 2022
- 稿件说明：
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肖进胜,张舒豪,陈云华等.双向特征融合与特征选择的遥感影像目标检测[J].电子学报,2022,50(02):267-272.

XIAO Jin-sheng,ZHANG Shu-hao,CHEN Yun-hua,et al.Remote Sensing Image Object Detection Based on Bidirectional Feature Fusion and Feature Selection[J].ACTA ELECTRONICA SINICA,2022,50(02):267-272.
肖进胜,张舒豪,陈云华等.双向特征融合与特征选择的遥感影像目标检测[J].电子学报,2022,50(02):267-272. DOI： 10.12263/DZXB.20210354.

XIAO Jin-sheng,ZHANG Shu-hao,CHEN Yun-hua,et al.Remote Sensing Image Object Detection Based on Bidirectional Feature Fusion and Feature Selection[J].ACTA ELECTRONICA SINICA,2022,50(02):267-272. DOI： 10.12263/DZXB.20210354.

摘要

遥感影像中复杂的背景占据图像的大部分区域，严重影响了目标检测效果.本文提出一种可以对特征图进行多特征选择的目标检测网络.设计了双向多尺度特征融合网络，融合深浅层信息，提高复杂背景下小目标的检测效果，在保留常规特征金字塔自上而下路径的同时，增加一条自下而上的路径，减少浅层特征传递到顶层经历的网络层数，从而控制浅层特征损失.为了降低多尺度特征图中无用信息对后续检测网络的干扰，设计了基于注意力机制的多特征选择模块，网络自适应地专注于有用特征，忽略无用特征.针对传统五参数回归法在预测角度时存在严重的边界不连续问题，不能精确预测长宽比值比较大的目标，将角度预测当作分类任务处理.在DOTA数据集和自制数据集DOTA-GF上进行实验，6类典型目标的mAP分别达到0.651和0.641，与主流目标检测算法的对比实验结果表明提出的方法的有效性.

Abstract

In remote sensing image object detection

the complex background always occupies a large area of the entire image

which seriously affects the object detection effect. This paper proposes an object detection network that can perform multiple feature fusion and selection on feature maps. A feature fusion network is used to fuse deep and shallow features to improve the detection effect of small objects in complex background. While retaining the up-bottom path of the feature fusion network

it adds a bottom-up path to diminish the number of network layers that the shallow features need to pass on to the top layer

thereby reducing the loss of shallow features. In order to reduce the interference of useless information in the fusion feature maps with detection network

a multiple feature selection module is designed. The attention mechanism in the multiple feature selection module enables the network to adaptively focus on more important features

ignore useless features. Since the conventional five-parameter regression method has serious boundary problems

the angle prediction is often inaccurate for objects with a large aspect ratio

to solve this problem

the proposed method treats angle prediction as a classification task. The mAP of our method on DOTA and self-made dataset DOTA-GF reaches 0.651 and 0.641

and the comparative experiments with mainstream object detection methods demonstrate the effectiveness of the proposed method.

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references

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