基于改进YOLOv8的遥感图像检测算法  

Remote sensing image detection algorithm based on improved YOLOv8

作　　者：宋树成 程换新[1] Song Shucheng;Cheng Huanxin(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,青岛266061

出　　处：《电子测量技术》2025年第3期52-59,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(62273192)项目资助。

摘　　要：针对小目标在遥感图像中的局限性,如图像背景复杂、小目标分布密集、目标尺度多样等问题,本文提出了一种基于YOLOv8n的改进算法。首先,设计了一个多尺度空洞注意力模块,在主干网络中引入多尺度空洞注意力机制与C2f模块结合,以有效捕捉多尺度的语义信息并减少自注意力机制的冗余;其次,设计了一个残差快速卷积模块,减小模型计算量并提高特征提取能力;最后,使用PIoU v2-Iou损失函数代替CIOU损失函数,提升模型的检测精度。通过在DOTA、RSOD和VisDrone2019数据集上的实验结果显示,改进后YOLOv8n模型与原模型YOLOv8n相比,mAP分别提升了2.7%、3.3%和3.8%,计算量降低了0.5 GFLOPs,验证了新算法的有效性。Aiming at the limitations of small targets in remote sensing images,such as complex image background,dense distribution of small targets,and diverse target scales,this paper proposes an improved algorithm based on YOLOv8n.Firstly,a multi-scale null attention module is designed to introduce a multi-scale null attention mechanism in the backbone network in combination with the C2f module to effectively capture multi-scale semantic information and reduce the redundancy of the self-attention mechanism;secondly,a residual fast convolution module is designed to reduce the model computation and improve the feature extraction capability;finally,the PIoU v2-Iou loss function is used instead of the CIOU loss function to improve the detection accuracy of the model.The experimental results on DOTA,RSOD and VisDrone2019 datasets show that the improved YOLOv8n model improves the mAP by 2.7%、3.3%and 3.8%,respectively,and reduces the computation by 0.5 GFLOPs compared with the original model YOLOv8n,which validates the effectiveness of the new algorithm.

关 键 词：遥感图像 YOLOv8 多尺度空洞注意力机制 损失函数 

分 类 号：TN919.5[电子电信—通信与信息系统]