基于YOLOv5的航空图像目标检测方法研究

作者： 文青.   来源： 东莞理工学院 年份： 2023 文献类型 ： 学位论文 关键词： Circular   损失函数   注意力机制   Transformer   YOLOv5   Smooth   Label   Swin  

描述： 基于YOLOv5的航空图像目标检测方法研究

基于YOLOv5的航空图像目标检测方法研究

作者： 文青.   来源： 东莞理工学院 年份： 2023 文献类型 ： 学位论文 关键词： Circular   损失函数   注意力机制   Transformer   YOLOv5   Smooth   Label   Swin  

描述： 基于YOLOv5的航空图像目标检测方法研究

EGMT-CD: Edge-Guided Multimodal Transformers Change Detection from Satellite and Aerial Images

作者： Yunfan   Xiang     Xiangyu   Tian     Yue   Xu     Xiaokun   Guan     Zhengchao   Chen   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： sensing   detection   images   edge   Transformer   feature   change   remote   alignment   heterogeneous  

描述： Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images.

基于YOLOv5的航空图像目标检测方法研究

作者： 文青.   来源： 东莞理工学院 年份： 2023 文献类型 ： 学位论文 关键词： Circular   损失函数   注意力机制   Transformer   YOLOv5   Smooth   Label   Swin  

描述： 基于YOLOv5的航空图像目标检测方法研究

基于YOLOv5的航空图像目标检测方法研究

作者： 文青.   来源： 东莞理工学院 年份： 2023 文献类型 ： 学位论文 关键词： Circular   损失函数   注意力机制   Transformer   YOLOv5   Smooth   Label   Swin  

描述： 基于YOLOv5的航空图像目标检测方法研究

基于YOLOv5的航空图像目标检测方法研究

作者： 文青.   来源： 东莞理工学院 年份： 2023 文献类型 ： 学位论文 关键词： Circular   损失函数   注意力机制   Transformer   YOLOv5   Smooth   Label   Swin  

描述： 基于YOLOv5的航空图像目标检测方法研究

基于YOLOv5的航空图像目标检测方法研究

作者： 文青.   来源： 东莞理工学院 年份： 2023 文献类型 ： 学位论文 关键词： Circular   损失函数   注意力机制   Transformer   YOLOv5   Smooth   Label   Swin  

描述： 基于YOLOv5的航空图像目标检测方法研究

EGMT-CD: Edge-Guided Multimodal Transformers Change Detection from Satellite and Aerial Images

作者： Yunfan   Xiang     Xiangyu   Tian     Yue   Xu     Xiaokun   Guan     Zhengchao   Chen   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： sensing   detection   images   edge   Transformer   feature   change   remote   alignment   heterogeneous  

描述： Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images.