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随着无人机、固定摄像头等多源监测设备普及，采集图像分辨率差异显著增大，其中小目标物体的监测常面临背景噪声、纹理模糊、漏检率高等困扰。因此，文章提出一种改进的WEMD-YOLOv11n小目标检测算法。首先，在C3k2模块中引入小波卷积，利用频域多频段分解强化不同分辨率下的小目标特征；其次，结合EMA注意力机制引导跨尺度跨层次特征融合，构建更高分辨率的三层检测输出，减少低分辨率状态下小目标细节信息丢失；最后，采用动态检测头提升不同分辨率下小目标的多维度感知及动态适应性。实验结果表明，改进的模型召回率、平均检测精度较YOLOv11n分别提升0.8%和3.8%，参数量减少32.6%。为跨分辨率场景下小目标监测需求提供了一种高精度、低资源消耗的解决方案。
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基本信息:
中图分类号:TP391.41
引用信息:
[1]闫建红,王嘉乐.WEMD-YOLOv11n：融合频域增强和动态感知的小目标检测算法[J].信息技术与信息化,2026,No.311(02):1-5.
基金信息:
山西省科技战略研究专项重点项目（202304031401011）; 山西省重点研发计划项目（202102010101008）; 山西省研究生精品教学案例项目（2024AL27）
2026-02-25
2026-02-25