改进YOLOv11n检测遥感图像中小目标和多尺度目标
Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images
- 在骨干网引入大可分离核注意力，增强小目标特征提取
- 结合Gold-YOLO与MultiSEAMHead，提升多尺度融合与检测能力
- 在DOTAv1数据集上[email protected]提升1.3%至1.8%，适合轻量遥感检测
卫星遥感图像因高分辨率、复杂场景及目标尺度差异大，给目标检测带来挑战。针对YOLOv11n模型在遥感图像中检测精度不足的问题，本文提出两种改进策略。方法一：(a) 在骨干网络中引入大可分离核注意力（LSKA）机制，增强小目标特征提取；(b) 在颈部网络集成Gold-YOLO结构，实现多尺度特征融合，提升不同尺度目标的检测性能。方法二：(a) 同样将Gold-YOLO嵌入颈部；(b) 结合MultiSEAMHead检测头，进一步强化小目标与多尺度目标的表征与检测能力。在DOTAv1数据集上的实验表明，所提方法在保持模型轻量化优势的同时，相较于基线YOLOv11n，[email protected]分别提升1.3%和1.8%，验证了其在遥感图像目标检测中的有效性与实用价值。
原文摘要 · Abstract (English)
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 ([email protected]) 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.
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