计算机应用与软件2024，Vol.41Issue(8)：149-154,6.DOI:10.3969/j.issn.1000-386x.2024.08.021
基于U形特征融合的遥感图像目标检测方法
U-SHAPE FUSION FEATURE BASED OBJECT DETECTION METHOD FOR REMOTE SENSING IMAGES
摘要
Abstract
Due to the particularity of remote sensing image,such as wide field of vision,small target,how to quickly and accurately detect targets in remote sensing images is still a challenging problem.A new method based on improved YOLOv3,U-YOLO,is presented.The selection method of anchor box was improved,and the problem of unbalanced selection of pre-selection box was solved.A U-shaped feature extraction module was proposed to extract deeper features and improve the detection effect.The area factor applied to the loss function was put forward,which improved the difficulty of small target detection.The experiments were conducted on the NWPU VHR-10 dataset and RSOD dataset.Experimental results show that this method is 0.079 and 0.065 higher than the original YOLOv3 in the two groups of experiments,respectively.关键词
目标检测/遥感图像/深度学习Key words
Object detection/Remote sensing image/Deep learning分类
信息技术与安全科学引用本文复制引用
尹雪乔,宋叔尼..基于U形特征融合的遥感图像目标检测方法[J].计算机应用与软件,2024,41(8):149-154,6.基金项目
国家自然科学基金项目(11801065). （11801065）
