遥感图像目标检测存在目标尺寸变化大、小目标排列密集、背景信息复杂等问题,针对这些问题,提出了端到端的遥感图像目标检测网络AMFI-RetinaNet(Attention and Multi-scale Feature Interactive-RetinaNet)来提高特征的判别能力。首先,...遥感图像目标检测存在目标尺寸变化大、小目标排列密集、背景信息复杂等问题,针对这些问题,提出了端到端的遥感图像目标检测网络AMFI-RetinaNet(Attention and Multi-scale Feature Interactive-RetinaNet)来提高特征的判别能力。首先,提出了卷积注意力来增强特征的空间信息,并结合通道注意力来增强特征的通道信息,该注意力模块位于特征提取网络的低层,能有效突出关键的细节特征,并提高网络对小目标的检测能力。此外,还提出了多尺度特征交互模板,通过相邻两层特征的交互,使低层特征信息流向高层,在特征金字塔结构后引入该模块,进一步提高了网络对多尺度目标的检测性能。在RSOD数据集和NWPU VHR-10数据集上进行实验,该方法比原RetinaNet网络的平均检测精度分别提升了2%和1.1%,实验结果表明提出的AMFI-RetinaNet网络可以更精确地对遥感图像目标进行检测和定位。展开更多
旋转目标检测是遥感图像处理领域中的重要任务,其存在的目标尺度变化大和目标方向任意等问题给自动目标检测带来了挑战。针对上述问题,提出了一种改进的RoI Transformer旋转目标检测框架:首先,利用RoI Transformer检测框架获取旋转的感...旋转目标检测是遥感图像处理领域中的重要任务,其存在的目标尺度变化大和目标方向任意等问题给自动目标检测带来了挑战。针对上述问题,提出了一种改进的RoI Transformer旋转目标检测框架:首先,利用RoI Transformer检测框架获取旋转的感兴趣区域特征(rotated region of interest,RRoI)用于鲁棒的几何特征提取;其次,在检测器中引入高分辨率网络(high-resolution network,HRNet)提取多分辨率特征图,在保持高分辨率特征同时适应目标的多尺度变化;最后,引入KLD(Kullback-Leibler divergence)损失,解决旋转目标表示的角度周期性的问题,提高检测方法对任意方向目标的适应性,并通过旋转目标边界框参数的联合优化提升目标定位精度。本文提出的旋转目标检测方法,即HRD-ROI Transformer(HRNet+KLD ROI Transformer),在DOTAv1.0和DIOR-R两个公开数据集上与典型的旋转目标检测方法进行了比较。结果显示:相比于传统的RoI Transformer检测框架,本文方法在DOTAv1.0和DIOR-R数据集上检测结果的mAP(mean-average-precision)分别提高了3.7%和4%。展开更多
In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have differ...In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.展开更多
文摘旋转目标检测是遥感图像处理领域中的重要任务,其存在的目标尺度变化大和目标方向任意等问题给自动目标检测带来了挑战。针对上述问题,提出了一种改进的RoI Transformer旋转目标检测框架:首先,利用RoI Transformer检测框架获取旋转的感兴趣区域特征(rotated region of interest,RRoI)用于鲁棒的几何特征提取;其次,在检测器中引入高分辨率网络(high-resolution network,HRNet)提取多分辨率特征图,在保持高分辨率特征同时适应目标的多尺度变化;最后,引入KLD(Kullback-Leibler divergence)损失,解决旋转目标表示的角度周期性的问题,提高检测方法对任意方向目标的适应性,并通过旋转目标边界框参数的联合优化提升目标定位精度。本文提出的旋转目标检测方法,即HRD-ROI Transformer(HRNet+KLD ROI Transformer),在DOTAv1.0和DIOR-R两个公开数据集上与典型的旋转目标检测方法进行了比较。结果显示:相比于传统的RoI Transformer检测框架,本文方法在DOTAv1.0和DIOR-R数据集上检测结果的mAP(mean-average-precision)分别提高了3.7%和4%。
文摘In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.
