Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images
This work addresses object detection challenges in remote sensing imagery for applications like satellite image analysis, but it is incremental as it builds upon the existing YOLOv11n model.
This paper tackled the problem of insufficient detection accuracy in remote sensing images by proposing two improvement strategies for the YOLOv11n model, resulting in mAP@0.5 increases of 1.3% and 1.8% on the DOTAv1 dataset while maintaining model lightweightness.
Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion, thereby improving the detection performance of objects at different scales. Method 2: (a) the Gold-YOLO structure is also integrated into the neck network; (b) a MultiSEAMHead detection head is combined to further strengthen the representation and detection capability for small and multi-scale objects. To verify the effectiveness of the proposed improvements, experiments are conducted on the DOTAv1 dataset. The results show that, while maintaining the lightweight advantage of the model, the proposed methods improve detection accuracy (mAP@0.5) by 1.3% and 1.8%, respectively, compared with the baseline YOLOv11n, demonstrating the effectiveness and practical value of the proposed approaches for object detection in remote sensing images.