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
为了提高激光点云的配准精度和效率,解决两片点云之间存在尺度变换的配准问题,提出了一种基于有向包围盒的尺度点云配准算法。首先,分别生成两片点云的空间有向包围盒,利用两个包围盒对应边的比值计算尺度因子。然后,将目标点云包围盒...为了提高激光点云的配准精度和效率,解决两片点云之间存在尺度变换的配准问题,提出了一种基于有向包围盒的尺度点云配准算法。首先,分别生成两片点云的空间有向包围盒,利用两个包围盒对应边的比值计算尺度因子。然后,将目标点云包围盒进行尺度放缩,再利用包围盒对应顶点的关系计算旋转矩阵。同时,引入点云的单位向量和,以单位向量和之间余弦相似度最大为准则,选择正确的旋转矩阵。最后,为了实现精确配准,将尺度因子引入点到面迭代最近点(Iterative Closest Point, ICP)算法中,利用加权最小二乘法求解变换参数。实验结果表明,在点云之间存在数据缺失、噪声干扰和尺度变换的情况下,所提算法可以实现快速精确配准,且具备良好的稳健性。展开更多
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.
文摘为了提高激光点云的配准精度和效率,解决两片点云之间存在尺度变换的配准问题,提出了一种基于有向包围盒的尺度点云配准算法。首先,分别生成两片点云的空间有向包围盒,利用两个包围盒对应边的比值计算尺度因子。然后,将目标点云包围盒进行尺度放缩,再利用包围盒对应顶点的关系计算旋转矩阵。同时,引入点云的单位向量和,以单位向量和之间余弦相似度最大为准则,选择正确的旋转矩阵。最后,为了实现精确配准,将尺度因子引入点到面迭代最近点(Iterative Closest Point, ICP)算法中,利用加权最小二乘法求解变换参数。实验结果表明,在点云之间存在数据缺失、噪声干扰和尺度变换的情况下,所提算法可以实现快速精确配准,且具备良好的稳健性。
