通信与信息技术Issue(1)：29-33,5.
基于改进YOLOv5的航拍图像检测方法
An aerial image detection method based on improved YOLOv5
摘要
Abstract
Due to issues such as occlusion and overlapping in aerial images,it is challenging for models to achieve stable recogni-tion,which reduces efficiency in areas such as military target tracking,traffic monitoring,and disaster observation.To address these problems,a method based on improved YOLOv5 for aerial image detection has been proposed.This method introduces a new convolu-tional neural network module(Space-to-depth Convolution,SPD-Conv)for low-resolution images and small objects,a small object de-tection head,a soft non-maximum suppression algorithm(Soft Non-maximum Suppression,Soft-NMS),and a regression loss function.Extensive experiments have been conducted on the VisDrone2019 dataset.The experimental results show that the proposed method achieves an average accuracy improvement of 12.5%and a 9.3%increase in mAP@0.5:0.95 metric on the VisDrone2019 dataset.关键词
小目标检测/SPD-Conv/Soft-NMS/回归损失函数Key words
Small target detection/SPD-Conv/Soft-NMS/Regression loss function分类
信息技术与安全科学引用本文复制引用
王嘉锵,刘子德,王绪娜,高宏伟..基于改进YOLOv5的航拍图像检测方法[J].通信与信息技术,2024,(1):29-33,5.基金项目
辽宁省重点科技创新基地联合开放基金,基于机器视觉的空间站机械臂定位技术研究(2021-KF-12-05) （2021-KF-12-05）
