计算机与数字工程2024，Vol.52Issue(2)：416-422,7.DOI:10.3969/j.issn.1672-9722.2024.02.022
基于改进YOLOv4算法的遥感图像飞机目标检测
Aircraft Object Detection Based on Improved YOLOv4 Algorithm for Remote Sensing Images
王惠中 1文学1
作者信息
- 1. 兰州理工大学电气工程与信息工程学院 兰州 730050||兰州理工大学甘肃省工业过程先进控制重点实验室 兰州 730050
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摘要
Abstract
Aiming at the problem of low accuracy of aircraft target detection on remote sensing images,this paper deepens the PANet feature fusion network structure to make the YOLOv4 algorithm more sensitive to the detection of small objects,thereby im-proving the average detection precision of the algorithm.In addition,the K-means++ algorithm is used to generate adaptive data sets.In order to reduce the redundancy of the YOLOv4 detection algorithm in the calculation of the bounding box regression loss.Comparative experiments on the RSOD data set show that the AP value of the improved algorithm reaches 80.25%.In particular,the improved YOLOv4 algorithm has a higher confidence score for small object detection respectively.关键词
遥感图像/目标检测/YOLOv4/特征融合Key words
remote sensing images/object detection/YOLOv4/feature fusion分类
信息技术与安全科学引用本文复制引用
王惠中,文学..基于改进YOLOv4算法的遥感图像飞机目标检测[J].计算机与数字工程,2024,52(2):416-422,7.
