A Remote Sensing Image Object Detection Model Based on Improved YOLOv11

Due to the challenges posed by high resolution, substantial background noise, significant object scale variation, and long-tailed data distribution in remote sensing images, traditional techniques often struggle to maintain both high accuracy and low latency. This paper proposes YOLO11-FSDAT, an advanced object detection framework tailored for remote sensing imagery, which integrates not only modular enhancements but also theoretical and architectural innovations to address these limitations. First, we propose the frequency–spatial feature extraction fusion module (Freq-SpaFEFM), which breaks the conventional paradigm of spatial-domain-dominated feature learning by introducing a multi-branch architecture that fuses frequency- and spatial-domain features in parallel. This design provides a new processing paradigm for multi-scale object detection, particularly enhancing the model’s capability in handling dense and small-object scenarios with complex backgrounds. Second, we introduce the deformable attention-based global–local fusion module (DAGLF), which combines fine-grained local features with global context through deformable attention and residual connections. This enables the model to adaptively capture irregularly oriented objects (e.g., tilted aircraft) and effectively mitigates the issue of information dilution in deep networks. Third, we develop the adaptive threshold focal loss (ATFL), which is the first loss function to systematically address the long-tailed distribution in remote sensing datasets by dynamically adjusting focus based on sample difficulty. Unlike traditional focal loss with fixed hyperparameters, ATFL decouples hard and easy samples and automatically adapts to varying class distributions. Experimental results on the public DOTAv1, SIMD, and DIOR datasets demonstrated that YOLO11-FSDAT achieved 75.22%, 82.79%, and 88.01% mAP, respectively, outperforming baseline YOLOv11n by up to 4.11%. These results confirm the effectiveness, robustness, and broader theoretical value of the proposed framework in addressing key challenges in remote sensing object detection.