基于YOLOv8改进的无人机视觉小目标检测模型

doi:10.12068/j.issn.1005-3026.2025.20240116

摘要/Abstract

摘要：

针对无人机航拍图像中小目标易误检和漏检的问题以及无人机检测对实时性和轻量化的需求，提出一种基于YOLOv8改进的轻巧高效模型.首先，将YOLOv8的Neck部分简化为特征金字塔网络，使模型有效利用浅层网络提取的细节信息，并增加特征融合模块为Head层提供更利于小目标检测的特征；其次，在Backbone部分集成高效局部注意力机制以实现对目标区域的精确定位.实验结果表明，与YOLOv8s相比，改进模型的参数量和模型规模分别降低50%，mAP_0.5和检测速度分别提升4%.该改进模型为无人机检测领域的部署提供了新思路.

关键词: 小目标检测, YOLOv8, 高效局部注意力机制, 模型轻量化, 无人机航拍

Abstract:

In view of easy false detection and missed detection of small objects in unmanned aerial vehicle （UAV） aerial images， as well as the requirements for real-time performance and lightweight design in UAV detection tasks， an improved lightweight and efficient model based on YOLOv8 was proposed. Firstly， the Neck part of YOLOv8 was simplified into a feature pyramid network， enabling the model to effectively utilize the detailed information extracted by shallow networks. Meanwhile， a feature fusion module was added to provide more favorable features for small object detection to the Head layer. Secondly， an efficient local attention （ELA） mechanism was integrated into the Backbone part to achieve accurate localization of target regions. Experimental results show that compared with YOLOv8s， the parameters and model size of the improved model are reduced by 50%， while the mAP_0.5 and detection speed are improved by 4%. This improved model provides a new idea for the deployment of UAV detection.

Key words: small object detection, YOLOv8, efficient local attention mechanism, lightweight model, UAV aerial photography

中图分类号:

TP 391.4

刘纪红, 时瑞瑞. 基于YOLOv8改进的无人机视觉小目标检测模型[J]. 东北大学学报（自然科学版）, 2025, 46(12): 29-37.

Ji-hong LIU, Rui-rui SHI. An Improved Small Object Detection Model Based on YOLOv8 for UAV Vision[J]. Journal of Northeastern University(Natural Science), 2025, 46(12): 29-37.

图/表 10

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环境	型号
GPU	NVIDIA GeForce RTX 4090
内存	24 GB
操作系统	Windows
系统位数	64
编译器	Pycharm 2023
CUDA	CUDA11.8
编译环境	Python3.9.19
深度学习框架	PyTorch2.2.2

环境	型号
GPU	NVIDIA GeForce RTX 4090
内存	24 GB
操作系统	Windows
系统位数	64
编译器	Pycharm 2023
CUDA	CUDA11.8
编译环境	Python3.9.19
深度学习框架	PyTorch2.2.2

YOLOv8版本	深度倍数	宽度倍数	比率
YOLOv8n	0.33	0.25	2.0
YOLOv8s	0.33	0.50	2.0
YOLOv8m	0.67	0.75	1.5
YOLOv8l	1.00	1.00	1.0
YOLOv8x	1.00	1.25	1.0

YOLOv8版本	深度倍数	宽度倍数	比率
YOLOv8n	0.33	0.25	2.0
YOLOv8s	0.33	0.50	2.0
YOLOv8m	0.67	0.75	1.5
YOLOv8l	1.00	1.00	1.0
YOLOv8x	1.00	1.25	1.0

参数	数值
训练轮数	250
批次大小	8
图像尺寸/像素	640×640
交并比	0.7
初始学习率	1×10^-2
最终学习率	1×10^-4
数据增强	1.0
动量	0.937