改进YOLOv8的无人机航拍小目标检测算法

doi:10.3778/j.issn.1002-8331.2411-0459

摘要/Abstract

摘要： 在无人机航拍图像目标检测任务中，存在小目标多且分布密集，目标背景复杂，类别样本数量不平衡，无人机算力偏低等问题。为此提出一种改进YOLOv8的算法MFF-YOLOv8（multi-feature fusion YOLOv8）。在C2f模块的Bottleneck模块中融合可变形卷积DCNv3（deformable convolution v3），增强模型主干部分的特征提取能力。设计了一种新的MFFPN（multi-feature fusion pyramid network）特征融合网络结构，增加更多特征融合路线，保留更多的底层特征图细节和特征，提高模型对小目标的检测能力。增加P2小目标检测层并优化原有的P5检测层，增强了对小目标的检测精度并降低参数量。最后，引入动态头Dyhead（dynamic head）进一步增强模型的检测精度，在Visdrone2019公共数据集的实验中，MFF-YOLOv8s算法的检测精度mAP50和mAP50：95相比YOLOv8s分别提高10.2个百分点和7.1个百分点，参数量降低77.04%，检测精度超越YOLOv11，满足了无人机平台对精度和轻量化的需求。

关键词: YOLOv8, 小目标检测, 多尺度特征融合, 轻量化

Abstract: Detecting small and densely distributed targets in UAV aerial images poses challenges such as complex backgrounds, imbalanced sample numbers, and limited computational power. To address these issues, an improved YOLOv8 algorithm, MFF-YOLOv8 (multi-feature fusion YOLOv8), is proposed. This algorithm integrates deformable convolution DCNv3 (deformable convolution v3) into the Bottleneck module of the C2f module to enhance feature extraction. A new MFFPN (multi-feature fusion pyramid network) is designed to add more feature fusion routes, retain low-level feature map details, and improve small target detection. Additionally, a P2 small target detection layer is added and the P5 detection layer is optimized to enhance accuracy and reduce parameters. The introduction of the Dyhead (dynamic head) further improves detection precision of the model. In the experiment on the Visdrone2019 dataset, MFF-YOLOv8s has a 10.2 percentage points and 7.1 percentage points increase in mAP50 and mAP50：95 respectively compared to YOLOv8s, with a 77.04% reduction in parameters. The model’s detection accuracy surpasses that of YOLOv11, meeting the precision and lightweight requirements for UAV platforms.

Key words: YOLOv8, small targets detection, multi-scale feature fusion, lightweight

许景科, 索祥龙, 周磊. 改进YOLOv8的无人机航拍小目标检测算法[J]. 计算机工程与应用, 2025, 61(11): 119-131.

XU Jingke, SUO Xianglong, ZHOU Lei. Improved YOLOv8 Algorithm for Small Target Detection in Drone Aerial Photography[J]. Computer Engineering and Applications, 2025, 61(11): 119-131.

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