Remote sensing image target detection model integrating dynamic receptive field and snake convolution

To address the challenges of a high missed detection rate for small targets and strong interference from complex backgrounds in remote sensing image target detection, the paper proposes an improved YOLOv11n based method. We introduce an enhanced YOLOv11n model incorporating a dynamic receptive field module (RFAConv) and a snake deformation modeling module (DySnakeConv). This approach strengthens shallow feature extraction capabilities and refines adaptive fitting of target boundaries, thereby improving detection accuracy. Experimental results demonstrate that on the RSOD dataset, the improved model achieves mean average precision (mAP) scores of 96.9 % at IoU = 0.50 (mAP50) and 65.5 % over IoU thresholds from 0.50 to 0.95 (mAP5095). These results surpass those of YOLOv8n, YOLOv10n, and other comparative models in key metrics such as precision and recall. Importantly, the model maintains comparable performance on the NWPU VHR-10 dataset. The proposed model presents an efficient solution for detecting small and geometrically sensitive targets in high-resolution remote sensing images.