面向遥感目标检测的无锚框Transformer算法OACSCDCSTPCD

Aerial remote sensing image targets have the characteristics of multi-directional arrangement,small,and dense.The rotating target detection algorithm based on deep learning has the problem of poor detection accuracy.To solve this problem,the article proposes a novel anchor-free Transformer algorithm for aerial remote sensing target detection.Firstly,hierarchical Transformer is used to collect feature information of different resolutions to improve the range of fea-ture information collection.Secondly,a new feedforward network(Spacial-FeedForward Neural network,SFFN)is con-structed.SFFN combines the local space characteristics of 3×3 depth separable convolution with the global channel charac-teristics of multi-layer perceptron(MLP)to solve the shortcomings of feed forward neural network(FFN)in local space modeling.Finally,an anchor-free detector is built based on SFFN architecture,and the regression problem of prediction frame is divided into horizontal frame and rotating frame,which alleviates the loss discontinuity problem of rotating frame.The test results on DOTA dataset show that the average accuracy of this method has reached 75.83%,respectively,while achieving 92.47%of 5 small targets on NWPU VHR-10 dataset,which is more competitive in remote sensing target detec-tion accuracy.