中北大学学报（自然科学版）2025，Vol.46Issue(6)：712-725,14.DOI:10.62756/jnuc.issn.1673-3193.2025.01.0005
基于YOLOv8的小目标检测方法研究
Research on Small Target Detection Method Based on YOLOv8
摘要
Abstract
In order to improve the accuracy and efficiency of small target detection,this paper proposed an improved detection method based on YOLOv8 to address the shortcomings of existing algorithms in small target recognition.Based on YOLOv8,this method integrated SPD(Space-to-Depth)module,which effectively avoided the information loss caused by traditional strided convolution and pooling opera-tion.At the same time,an improvement of Fractional Fourier Transform Convolution(FT_Conv)was proposed to improve the detection accuracy and computational efficiency of the model for small targets.In addition,the C2f_BiLevel Routing Attention mechanism was used to realize dynamic sparse attention,optimize the feature fusion and object detection performance,and further improve the recognition ability of the model for small targets.Finally,the Powerful-IoU loss function was introduced to improve the area expansion of the anchor frame of the existing IoU and enhance the focusing ability of the anchor frame.The experimental results show that compared with the original YOLOv8 model,the average accuracy(AP)of the improved model in small target detection tasks is increased by 3.29 percentage points,and the false detection and missed detection rates are significantly reduced.These results confirm that the improved YOLOv8 model has obvious performance advantages in the field of small target detection.关键词
小目标检测/YOLOv8/分数阶傅里叶变换/模型评估/动态稀疏注意力/Powerful-IoU损失函数Key words
small target detection/YOLOv8/FT_Conv/model evaluation/dynamic sparse attention/Powerful-IoU loss function分类
信息技术与安全科学引用本文复制引用
王晓雯,常居泰,于骐瑞..基于YOLOv8的小目标检测方法研究[J].中北大学学报（自然科学版）,2025,46(6):712-725,14.基金项目
山西省基础研究计划资助项目(202403021221010) （202403021221010）
