To address the issues of imprecise target localization, missed detections, and false alarms caused by significant differences in target scales, diverse categories, and uneven target distribution in remote sensing images, this paper proposes an improved algorithm based on YOLOv8n, named MGD-YOLO. Firstly, the multi-scale edge-gaussian attention module （MEGA） is introduced. By integrating Gaussian smoothing, the Scharr edge operator, and a channel attention mechanism, MEGA effectively suppresses noise and enhances the feature representation of target contours in complex backgrounds. Secondly, the MDPConv structure is designed, which combines a dynamic weighted fusion mechanism with depthwise separable convolutions to overcome the fixed receptive field problem of traditional convolutions and improve the model′s ability to detect targets of varying scales. Lastly, the DLGA structure is introduced in the detection head. By dynamically allocating weights to multiple attention branches and utilizing an MLP fusion strategy, DLGA significantly improves the integration of local and global features, thereby boosting detection performance. Experimental results demonstrate that MGD-YOLO achieves a 1.6%, 2.7% and 1% increase in mAP@0.5 on the DIOR, DOTA and NWPU VHR-10 datasets, respectively, compared to YOLOv8n, thus validating its effectiveness for remote sensing image target detection tasks.