Object detection in high-resolution remote sensing images based on multi-feature fusion and twin attention network
In order to improve the effect of object detection in high-resolution remote sensing images,this paper proposes a new object detection method by combining multi-feature fusion method and twin attention network.The overall framework of remote sensing image target detection is constructed,and the multi-layer features of remote sensing image target are extracted and fused based on the anchor frame model.The twin attention network is used for real-time visual tracking and detection of remote sensing image targets,and the dual self-attention mechanism of channel and space is introduced to improve the feature expression ability of target images,so as to get more accurate detection results.Through the analysis of experiments,the average overall accuracy of the proposed method is 93.8,the average F1 index is 0.88,and the average Kappa coefficient is 0.93,which are significantly higher than the comparison method,indicating that the proposed method has a good detection effect.
multi-feature fusionhigh resolutionremote sensing imagetwin attentionobject detectionsemantic features
万方数据
维普
