郑州大学学报（工学版）2025，Vol.46Issue(4)：62-69,8.DOI:10.13705/j.issn.1671-6833.2025.04.007
基于FEW-YOLOv8遥感图像目标检测算法
Target Detection Algorithm Based on FEW-YOLOv8 Remote Sensing Images
摘要
Abstract
Aiming at the problems of lack of small target information during feature extraction,partial loss of infor-mation during feature fusion,and inconspicuous small target feature information in remote sensing image target de-tection task,which lead to the low accuracy of small target detection,an algorithm for remote sensing image target detection based on FEW-YOLOv8 model was proposed.Firstly,the backbone network architecture was optimized to use the FasterNet backbone network,which extracted the spatial features of small targets in remote sensing images more efficiently,making the network model more focused on tiny targets,thus improving the small target detection accuracy.Secondly,the new C2f_EMA module was constructed using EMA attention and C2f to replace the C2f module in Neck network,and the feature attention enhancement operation was performed before fusing the features,so that the network model highlighted the small-target part of the feature information more,which effectively solved the problem of small-target feature loss in the process of feature fusion.Finally,WIoUv3,which had a dynamic non-monotonic FM,was used as the bounding box loss function to improve the accuracy of the model's bounding box localization and strengthen the localization ability of small targets.The experimental results on NWPU VHR-10,HR-SC2016 and DOTA v1.0 datasets showed that the test mAP50 of the improved YOLOv8 algorithm was 7.71,9.70 and 12.32 percentage points higher than that of the original YOLOv8 algorithm,respectively,which proved that the pro-posed algorithm could effectively improve the detection accuracy of small targets in remote sensing images.关键词
遥感图像/YOLOv8/FasterNet骨干网络/EMA注意力机制/WIoU损失函数Key words
remote sensing images/YOLOv8/FasterNet backbone network/EMA attention mechanism/WIoU loss function分类
信息技术与安全科学引用本文复制引用
席阳丽,屈丹,王芳芳,都力铭..基于FEW-YOLOv8遥感图像目标检测算法[J].郑州大学学报（工学版）,2025,46(4):62-69,8.基金项目
国家自然科学基金资助项目(62171470) （62171470）
河南省中原科技创新领军人才项目(234200510019) （234200510019）
河南省自然科学基金资助项目(232300421240) （232300421240）
