摘要
遥感图像中的旋转目标检测由于存在背景复杂、目标在任意方向分布且密集排列、尺度变化剧烈、高长宽比等问题而具有挑战性。针对这些问题,提出基于多尺度感知增强的旋转目标检测框架。首先,在特征提取阶段,提出多尺度感知增强模块,针对不同层级的特征图采用不同的卷积块来提取特征,确保低层特征图能保留足够的细节信息,高层特征图能提取足够的语义信息,使得提取的多级特征图对不同尺度具有自适应的特征学习能力。同时,利用自适应通道注意力模块来学习通道权重,缓解复杂背景带来的影响。其次,提出尺寸敏感的旋转交并比损失,通过在旋转交并比损失中增加目标长宽比和面积的损失项,来监督网络学习目标的尺寸信息,增加对高长宽比目标的敏感性。在公开的遥感图像数据集DOTA,HRSC2016和DIOR-R上,所提方法分别取得77.64%,98.32%和66.14%的mAP,检测精度优于现有的先进遥感图像检测网络。
Oriented object detection in remote sensing images is more challenging due to the issues of complex background,dense distribution and with arbitrary direction,large-scale variation,high aspect-ratio of objects.To address these issues,this paper proposes a framework for oriented object detection in remote sensing images based on multi-scale perception enhancemen.Firstly,a multi-scale perceptual enhancement module is proposed in the feature extraction stage,which employs different convolutional blocks for extracting features for different levels of feature maps to ensure that the low-level feature maps retain enough detail information and the high-level feature maps extract enough semantic information.So that the extracted multilevel feature maps have the ability of adaptive feature learning for different scales.Meanwhile,an adaptive channel attention module is used to adaptively learn the channel weights to mitigate the effects of the complex background.Secondly,a size-sensitive rotated Itersection over Union(IoU)loss is proposed to supervi se the network to learn the size information of the target and increase the sensitivity to high aspect ratio targets by adding the loss terms of objects’aspect ratio and area in the loss.The proposed method achieves 77.64%,98.32%,and 66.14%mAP on t he publicly available remote sensing image datasets DOTA,HRSC2016,and DIOR-R,respectively.The detection accuracies of the proposed framework outperform existing state-of-the-art remote sensing image detection networks.
作者
张达斌
吴秦
周浩杰
ZHANG Dabin;WU Qin;ZHOU Haojie(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,Jiangsu 214122,China;Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,Wuxi,Jiangsu 214122,China)
出处
《计算机科学》
北大核心
2025年第6期247-255,共9页
Computer Science
关键词
遥感图像
旋转目标检测
多尺度感知增强
自适应特征学习
旋转交并比损失
Remote sensing image
Oriented object detection
Multi-scale perceptual enhancement
Adaptive feature learning
Rotated IoU loss
