Remote Sensing Target Detection Based on Multilevel Self- Attention Enhancement

Remote sensing image target detection technology has gained considerable attention with the improvement of remote sensing image resolution. This thesis proposes a remote sensing target detection algorithm based on multilevel local self- attention enhancement to solve such problems as complex background noise, arbitrary target direction, and large changes in target size in remote sensing images. First, the proposed algorithm adopts the Swin Transformer feature extraction module in an Oriented region- based convolutional neural network (R-CNN) backbone network, and the multilevel local information of feature-extracted semantic information is modeled using the Transformer module with shifted window operations and hierarchical design. Second, Oriented RPN is used to generate high-quality directed candidate boxes. Finally, the Kullback-Leibler divergence ( KLD) between Gaussian distributions is regarded as the regression loss function, allowing the parameter gradient to be dynamically adjusted based on the object's characteristics for more accurate regression of the detection boxes. The mean average precision (mAP) of the proposed algorithm reaches 77. 2% and 90. 6% on the DOTA dataset and HRSC2016 dataset, respectively, and it is increased by 1. 8 percentage points and 0. 5 percentage points compared with the Oriented R-CNN algorithm. The results reveal that the proposed algorithm can effectively advance the target detection accuracy of remote sensing images.