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
This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. ...This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. It not only has higher speed but also can extract the edge better. Finally, an example of 2D image is given to demonstrate the usefulness and advantages of the algorithm.展开更多
Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and aco...Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and acoustic shadow are present A novel approach for hard plaque recognition and media-adventitia border detection of IVUS images is presented in this paper. The IVUS images were first enhanced by a spatial-frequency domain filter that was constructed by the directional filter and histogram equalization. Then, the hard plaque was recognized based on the intensity variation within different regions that were obtained using the k-means algorithm. In the next step, a cost matrix representing the probability of the media-adventitia border was generated by combining image gradient, plaque location and image intensity. A heuristic graph-searching was applied to find the media-adventitia border from the cost matrix.Experiment results showed that the accuracy of hard plaque recognition and media-adventitia border detection was 89.94% and 95.57%, respectively. In conclusion,using hard plaques recognition could improve media-adventitia border detection in IVUS images.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This paper presents an algorithm of edge detection in image processing. A new entropy operator and threshold estimation technique are effectively proposed. The algorithm overcomes some drawbacks of Shiozaki operator. It not only has higher speed but also can extract the edge better. Finally, an example of 2D image is given to demonstrate the usefulness and advantages of the algorithm.
文摘Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and acoustic shadow are present A novel approach for hard plaque recognition and media-adventitia border detection of IVUS images is presented in this paper. The IVUS images were first enhanced by a spatial-frequency domain filter that was constructed by the directional filter and histogram equalization. Then, the hard plaque was recognized based on the intensity variation within different regions that were obtained using the k-means algorithm. In the next step, a cost matrix representing the probability of the media-adventitia border was generated by combining image gradient, plaque location and image intensity. A heuristic graph-searching was applied to find the media-adventitia border from the cost matrix.Experiment results showed that the accuracy of hard plaque recognition and media-adventitia border detection was 89.94% and 95.57%, respectively. In conclusion,using hard plaques recognition could improve media-adventitia border detection in IVUS images.
