黑龙江科技大学学报2024，Vol.34Issue(6)：985-989,5.DOI:10.3969/j.issn.2095-7262.2024.06.025
改进YOLOv8的无人机小目标检测方法
Detection method of UAV at small target based on improved YOLOv8
摘要
Abstract
This paper aims to address the low detection accuracy,missed detection and false detec-tion at the small targets photographed by UAV with the characteristics of distribution clustering,large number,and unbalanced categories,and proposes a target detection algorithm based on improved YOLOv8.The study involves optimizing the network structure by adding a small target feature integrated network;introducing the deformable convolution to improve the ability of the model at the region focused;and improving the accuracy of bounding box regression by using MPDIoU loss function.The results show that the improved YOLOv8 detection algorithm improves the accuracy of the VisDrone2019 dataset by 6.1%,and the model parameters are reduced by 25.3%,as which effectively improves the accuracy of small target detection while lightweighting the network.关键词
小目标检测/YOLOv8/可变形卷积/损失函数Key words
small object detection/YOLOv8/deformable convolution/loss function分类
信息技术与安全科学引用本文复制引用
刘付刚,刘巾瑞,祝永涛..改进YOLOv8的无人机小目标检测方法[J].黑龙江科技大学学报,2024,34(6):985-989,5.基金项目
黑龙江省省属本科高校基本科研业务费项目(11040168) （11040168）
