福建师范大学学报（自然科学版）2024，Vol.40Issue(1)：76-86,11.DOI:10.12046/j.issn.1000-5277.2024.01.009
基于YOLOv5的无人机航拍改进目标检测算法Dy-YOLO
Dy-YOLO:Improved UAV Image Target Detection Algorithm Base on YOLOv5
摘要
Abstract
UAV aerial photography presents significant challenges,including intricate and diverse scenes,significant variations in target scale,and high-speed,low-altitude motion blur.To address poor performance of object detection in UAV aerial photography,the Dy-YOLO model is introduced.It integrates Dynamic Head attention into YOLOv5,exploring the potential of prediction heads with attention mechanisms related to scale awareness,spatial location,and multitasking.Additionally,a C3-DCN structure and Dynamic Head attention complement each other,enhancing feature extraction performance.The SimOTA label assignment compensates for small sample losses,while the CARAFE(Content-Aware ReAssembly of FEatures)upsampling operator effectively improves the fusion of convolutional feature maps.Dy-YOLO achieves an average mean accuracy of 38.2%on the VisDrone2019 test set,marking a 7.1 percentage points improvement over the baseline YOLOv5 method.It also outperforms the majority of object detection algorithms,demonstrating its effectiveness in UAV aerial photography object detection tasks.关键词
目标检测/注意力机制/无人机航拍/YOLOv5/可变形卷积网络Key words
object detection/attention mechanism/unmanned aerial vehicle(UAV)/YOLOv5/deformable convolution networks分类
数理科学引用本文复制引用
杨秀娟,曾智勇..基于YOLOv5的无人机航拍改进目标检测算法Dy-YOLO[J].福建师范大学学报（自然科学版）,2024,40(1):76-86,11.基金项目
福建省自然科学基金资助项目(2022J01187) （2022J01187）
福建省引导性项目(2021Y0011) （2021Y0011）
