LDA-YOLO: A YOLO-Based Rotated Object Detection Method for Remote Sensing with Large Kernel Attention and Deformable Alignment

Rotated object detection is widely adopted in remote sensing to handle arbitrary object orientations and improve localization accuracy. However, existing methods still suffer from limited global context modeling, degraded feature representation under complex backgrounds, and suboptimal optimization caused by task coupling, which jointly restrict detection performance in challenging scenarios. To address these issues, this paper proposes a novel rotated object detection framework, termed LDA-YOLO