红外技术2026，Vol.48Issue(9)：1115-1124,10.
改进YOLOv8的多尺度特征协同小目标检测算法
The Multi-Scale Feature Cooperative Small Target Detection Algorithm of Improved YOLOv8
摘要
Abstract
To address problems such as low accuracy,a high missed detection rate,and model redundancy in UAV aerial image detection,an improved multi-scale feature collaborative MSF-YOLO detection algorithm based on YOLOv8 was proposed in this study.First,the RepViTBlock and EMA attention mechanisms were used to construct RBE-C2f to replace the C2f module in the backbone network and improve feature extraction capability.Second,the neck network was reconstructed using the TFE,SSFF,and small-target detection layers to achieve multi-feature interactive fusion.Finally,a shared convolution detection head was designed in the detection head to make different detection heads share features,reduce redundant features,and improve the efficiency of the model detection.Compared with the original model in the VisDrone2019 data set,the accuracy rate,recall rate,mAP50,and mAP50-95 of the improved model increased by 3.1%,4.0%,4.6%,and 2.8%,respectively;the number of parameters decreased by 42%,and the model size decreased by 36%to only 3.8 MB.Based on an experimental comparison in a complex environment,the improved model exhibited a better detection effect than the basic YOLOv8 algorithm.关键词
YOLOv8/小目标检测/无人机/共享卷积Key words
YOLOv8/small target detection/drones/shared convolution分类
信息技术与安全科学引用本文复制引用
侯汶伶,许滌平,何兆益,林永盛,李圣博..改进YOLOv8的多尺度特征协同小目标检测算法[J].红外技术,2026,48(9):1115-1124,10.基金项目
国家重点研发计划项目(2018YFB1600201) （2018YFB1600201）
贵州省交通运输厅科技项目(2023-122-023). （2023-122-023）
