聚焦小目标的航拍图像目标检测算法

张智; 易华挥; 郑锦

doi:10.12263/DZXB.20220313

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聚焦小目标的航拍图像目标检测算法

学术论文 | 更新时间：2025-12-08

- 聚焦小目标的航拍图像目标检测算法
- Focusing on Small Objects Detector in Aerial Images
- 电子学报 2023年51卷第4期页码：944-955
- 作者机构：
  
  1.中国民航大学计算机科学与技术学院，天津 300300
  2.北京航空航天大学计算机学院，北京 100191
- 作者简介：
  
  [ "张智男，1993年12月出生于山西省大同市.现为中国民航大学计算机科学与技术学院实验师.主要研究方向为视频图像目标检测.E-mail: zhangz@cauc.edu.cn" ]
  [ "易华挥男，1997年11月生于四川省南充市.现为中国民航大学本科学生.主要研究方向为目标检测.E-mail: huahui_yi@163.com" ]
  [ "郑锦（通讯作者）女，1978年10月出生于四川省乐山市.现为北京航空航天大学计算机学院副教授、博士生导师.主要研究方向为计算机视觉、视频图像处理等." ]
- 基金信息：
  
  国家重点研发计划(2020YFB1600101);国家自然科学基金(61876014)
- DOI：10.12263/DZXB.20220313
  中图分类号： TP391.4;
- 收稿：2022-03-26，
  
  修回：2022-05-10，
  
  纸质出版：2023-04-25
- 稿件说明：
移动端阅览
张智,易华挥,郑锦.聚焦小目标的航拍图像目标检测算法[J].电子学报,2023,51(04):944-955.

ZHANG Zhi,YI Hua-hui,ZHENG Jin.Focusing on Small Objects Detector in Aerial Images[J].ACTA ELECTRONICA SINICA,2023,51(04):944-955.
张智,易华挥,郑锦.聚焦小目标的航拍图像目标检测算法[J].电子学报,2023,51(04):944-955. DOI： 10.12263/DZXB.20220313.

ZHANG Zhi,YI Hua-hui,ZHENG Jin.Focusing on Small Objects Detector in Aerial Images[J].ACTA ELECTRONICA SINICA,2023,51(04):944-955. DOI： 10.12263/DZXB.20220313.

摘要

与通用目标检测不同，无人机（Unmanned Aerial Vehicle，UAV）航拍图像目标检测主要面临两个难题：（1）远距离观察下存在大量小尺寸目标，难以与背景区分；（2）大量区域中目标密集且存在严重遮挡.因此，将通用目标检测器直接应用于航拍图像会导致检测精度下降.本文提出一种聚焦小目标的航拍图像目标检测算法（Focusing on Small objects Detector in aerial images，FocSDet）.针对小目标，通过密集高级组合（Dense Higher-Level Composition，DHLC）模式连接双Swin-Transfomer骨干网络，并和特征金字塔（Feature Pyramid Networks，FPN）结合，构建小目标特征聚合网络作为FocSDet的骨干网络，可丰富单层特征表达并提升对图像全局信息的利用，在不损失大目标语义信息的同时得到对小目标更好的特征描述，有效提升了小目标检测能力；针对区域密集遮挡，提出任务平衡样本分配策略，区别于现有样本分配策略只依赖定位位置，本文所提出的策略中样本匹配质量评价分数由定位位置信息和预测分类分数共同构成.基于该新评价分数不断迭代更新样本分配和监督网络优化，取得了更高质量的预测结果.最后，在检测头的分类和回归分支中引入层注意力构成增强检测头，进一步提升了小目标的检测性能.在Visdrone无人机数据集、CARPK航拍数据集上的实验表明，本文提出的FocSDet相较于现有方法ATSS和VFNET，在Visdrone上平均精度（Average Precision，AP）分别提升2%和0.6%，小目标AP

分别提升2.6%和1.2%；在CARPK上AP分别提升2.2%和1.7%，小目标AP

分别提升5.2%和5.0%.

Abstract

Different from general object detection in natural images

object detection in unmanned aerial vehicle (UAV) aerial images mainly faces these challenges such as large number of small objects in remote observation

which is difficult to distinguish from the background

and dense objects with serious occlusion in

lots of areas. Therefore

the direct application of general object detector to aerial images will lead to the decline of detection performance. In this paper

an aerial image object detection algorithm focusing on small objects (FocsDet) is proposed. For small objects

a small object feature aggregation network is designed

which connects the dual Swin-Transfomer backbone network through dense higher-level composition (DHLC) mode and combines with feature pyramid networks (FPN)

so as to improve the utilization of global image information

enrich single-layer feature expression

and obtain better feature description of small objects without losing semantic information of large objects. It effectively improves the detection performance of small object. For regional dense occlusion

a task-balance label assignment is proposed

in which the label matching quality evaluation score is composed of location cost and classification cost

which is different from the existing evaluation score which only depends on location cost. Based on the evaluation score

label assignment and supervision network optimization are updated iteratively

so as to achieve better prediction results. Finally

layer attention is introduced into the classification and regression branches of the detection head to form enhanced detection head

which further improves the detection performance of small objects. Experiments on Visdrone dataset and CARPK dataset show that compared with the existing methods such as ATSS and VFNET

the average precision (AP) of FocsDet is improved by 2% and 0.6%

is improved by 2.6% and 1.2% on Vsidrone dataset respectively. On CARPK dataset

AP increases by 2.2% and 1.7%

and AP

increases by 5.2% and 5.0% respectively.

关键词

Keywords

references

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