摘要
随着无人机(UAV)遥感技术的发展,无人机航拍图像目标检测逐渐成为无人机应用领域的一项核心技术,在交通规划、军事侦查及环境监测等领域具有重要应用价值。针对无人机图像中小目标实例多、背景复杂及特征提取困难的问题,提出一种基于多尺度分割注意力的无人机航拍图像目标检测算法MSA-YOLO。首先,利用嵌入在骨干网络瓶颈层的多尺度分割注意力单元建立多尺度特征间的远程依赖关系,从而强化关键特征的表达能力并抑制背景噪声干扰;其次,设计了一种自适应加权特征融合方法,该方法动态的优化各输出特征层权重,实现浅层特征与深层特征的深度融合;最后,在VisDrone公开数据集上的实验结果表明:该方法取得了34.7%的平均均值精度(mAP),相比于基线算法YOLOv5提高了2.8%,在复杂背景下仍能显著提升无人机图像目标检测性能。
With the development of Unmanned Aerial Vehicle(UAV)remote sensing technology,UAV aerial image object detection has become a core technology in the field of UAV applications such as traffic planning,military reconnaissance and environmental monitoring.To overcome the problem of difficulty in feature extraction due to many instances of small objects and complex background in UAV images,this paper proposes an object detection algorithm for UAV aerial images based on multi-scale split attention,i.e.,MAS-YOLO.Firstly,the multi-scale split attention unit embedded in the bottleneck layer of the backbone net-work is used to establish the long-range dependency relationship between different scales of attention,so as to enhance the expression ability of key features and suppress the interference of background noise.Secondly,an adaptive weighted feature fusion method is designed,which dynamically optimizes the weight of each output feature layer and realize the deep fusion of shallow and deep features.Finally,experimental results on the VisDrone public data set show that the proposed method achieves 34.7%mean Average Precision(mAP),which is 2.8%higher than that of the baseline algorithm YOLOv5,and can also significantly improve the performance of UAV image object detection in complex background.
作者
冒国韬
邓天民
于楠晶
Guotao MAO;Tianmin DENG;Nanjing YU(School of Traffic and Transportation,Chongqing Jiaotong University,Chongqing 400074,China;School of Automation,Chongqing University,Chongqing400044,China;School of Shipping and Naval Architecture,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2023年第5期268-278,共11页
Acta Aeronautica et Astronautica Sinica
基金
国家重点研发计划(SQ2020YFF0418521)
重庆市技术创新与应用发展专项(cstc2020jscx-dxwtBX0019)
川渝联合实施重点研发项目(cstc2020jscx-cylhX0007)。
关键词
无人机图像
计算机视觉
目标检测
注意力机制
自适应加权特征融合
unmanned aerial vehicle image
computer vision
object detection
attention mechanism
adaptive weighted feature fusion