Aircraft Detection and Recognition in SAR Images Based on Improved YOLOv11

Aircraft target detection and recognition in SAR images holds significant value across multiple domains. This paper improves the YOLOv11 object detection model through algorithmic modifications and applies it to aircraft target detection, achieving promising detection results. The proposed approach demonstrates improvements across multiple core evaluation metrics of object detection, compared to the original YOLOv11, the precision has improved by 0.5%, the recall rate has increased by 1.8%, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{mAP}_{\mathbf{0.5}}$</tex> has increased by 1.5%, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{mAP}_{\mathbf{0.5:0.9}}$</tex> has increased by 1.4%, thereby validating its effectiveness and potential application prospects.