MAEF-Net: An Efficient Multi-Scale Attention-Enhanced Feature Fusion Network for Remote Sensing Object Detection

(1) Objective: Remote sensing object detection faces significant challenges, including complex background interference, large variations in target scales, and insufficient multi-scale feature representation, which often result in missed detections of small objects, inaccurate localization, and inadequate feature fusion. (2) Methods: To address these issues, this paper proposes a Multi-scale Attention Enhancement Feature Fusion Network (MAEF-Net) based on YOLOv12. The proposed method systematically optimizes the overall detection pipeline from three aspects, namely feature enhancement, feature fusion, and prediction refinement. Specifically, it enhances local texture representation and high-level semantic information in the backbone, improves multi-scale feature interaction during the feature fusion stage, and adaptively filters fused features in the detection stage to strengthen target responses while suppressing background noise, thereby improving the detection performance for complex scenes, small objects, and densely distributed targets. (3) Results: To validate the effectiveness of the proposed method, extensive experiments are conducted on two public remote sensing object detection datasets, namely RSOD and NWPU VHR-10. Experimental results demonstrate that, compared with the baseline YOLOv12, MAEF-Net improves the mAP by 1.86% and 1.05% on the RSOD and NWPU VHR-10 datasets, respectively. Furthermore, compared with the latest YOLOv13, the proposed method achieves additional mAP improvements of 1.53% and 0.43%, respectively. Moreover, MAEF-Net achieves a favorable balance between detection accuracy and computational efficiency while maintaining relatively low computational complexity and high inference speed. Ablation studies, comparative experiments, and qualitative visualization further demonstrate the effectiveness, robustness, and generalization capability of the proposed method in complex remote sensing scenarios.