Remote Sensing Image Target Detection Model Based on Attention and Feature Fusion

Aiming at the problem that remote sensing images with complex environmental backgrounds and small targets are difficult to perform accurate target detection, based on the single-stage detection model (SSD), a singlestage target detection model based on attention and feature fusion is proposed in this paper, which is mainly composed of detection branch and attention branch. First, the attention branch is added to the detection branch SSD. The fully convolutional network (FCN) of the attention branch obtains the location characteristics of the target to be detected through pixel-by-pixel regression. Second, by using the method of adding corresponding elements to the detection branch and attention branch, the feature fusion of detection branch and attention branch are carried out to obtain high-quality feature image with more detailed information and semantic information. Finally, soft nonmaximum suppression (Soft-NMS) is used as a post-processing part to further improve the accuracy of target detection. Experimental results show that the mean average accuracy of the model on the UCAS-AOD and NWPU VHR-10 data sets are 92.52% and 82.49%, respectively. Compared with other models, the detection efficiency of the model is higher.