计算机工程与应用2025，Vol.61Issue(11)：93-104,12.DOI:10.3778/j.issn.1002-8331.2412-0324
融合多注意力机制的轻量级无人机航拍小目标检测模型
Lightweight UAV Aerial Small Object Detection Model Integrating Multi-Attention Mechanisms
摘要
Abstract
Object detection in aerial imagery captured by unmanned aerial vehicles(UAVs)faces significant challenges,such as detecting small-scale objects,handling variations in object sizes,and addressing limited computational resources.Existing small-object detection models,often large and computationally demanding,are unsuitable for deployment on edge devices.To address these limitations,the paper proposes a lightweight model,MA-YOLOv11s(multi-attention YOLOv11s),which builds on enhancements to YOLOv11.Firstly,it introduces selective small-object detection layers to improve performance while managing computational complexity.Secondly,this paper designs two lightweight feature extraction modules,C2SCSA and C2MCA,which integrate multiple attention mechanisms to enhance feature extraction for small objects in complex backgrounds while minimizing computational cost.Finally,it replaces the traditional NMS method with Soft-NMS-SIOU,significantly improving detection accuracy and robustness in scenarios with densely over-lapping objects.In experiments on the VisDrone2019 dataset,compared to the YOLOv11s model,MA-YOLOv11s achieves improvements of 8.9,1.3,10.9,and 9.7 percentage points in precision,recall,mAP50,and mAP50:95,respectively,with only 2.291×106 parameters and 22.4 GFLOPs of computation.The experimental results show that the improved model demonstrates exceptional small object detection performance while maintaining a compact size.关键词
无人机(UAV)/小目标检测/注意力机制/轻量化/YOLOv11Key words
unmanned aerial vehicle(UAV)/small object detection/attention mechanism/lightweight/YOLOv11分类
计算机与自动化引用本文复制引用
涂育智,王法翔,吴春霖..融合多注意力机制的轻量级无人机航拍小目标检测模型[J].计算机工程与应用,2025,61(11):93-104,12.基金项目
福建省科技计划项目(2023H4005). （2023H4005）
