计算机与现代化Issue(2)：43-49,63,8.DOI:10.3969/j.issn.1006-2475.2024.02.007
用于遥感图像目标检测的少样本算法
Few-shot Algorithm for Object Detection in Remote Sensing Images
摘要
Abstract
In view of the lack of remote sensing scene data,the obvious size change of surface objects captured by aerial photogra-phy,including a large number of objects of multiple categories and complex background,resulting in low detection accuracy and inaccurate classification,a small sample remote sensing target detection network based on the two-stage detection model(Faster RCNN)is proposed.New involution convolution operators are added to build detector backbone to improve feature extraction ca-pability;Integrate multi-scale object-level positive sample features to enhance the original features,suppress the adverse effects of negative samples,fully mine the feature information of each target scale,and help the semantic information to locate;The idea of comparative supervision is adopted to improve the loss function,refine the target classification and reduce the false detection rate.The experimental results on public remote sensing data sets show that the network can adapt to the multi-scale characteris-tics of remote sensing images and effectively alleviate the over-fitting phenomenon caused by data scarcity under the condition of only a small number of remote sensing labeled samples.Compared with the previous Meta RCNN and FsDet networks,the aver-age accuracy has been further improved by 3.8 percentage points and 2.5 percentage points,providing a meaningful reference for image target detection in the remote sensing field.关键词
少样本/目标检测/特征增强/微调/遥感图像/对比损失Key words
few shot/object detection/feature enhancement/fine tuning/remote sensing images/contrastive loss分类
信息技术与安全科学引用本文复制引用
薛杨义,周立凡,龚声蓉..用于遥感图像目标检测的少样本算法[J].计算机与现代化,2024,(2):43-49,63,8.基金项目
国家自然科学基金资助项目(61972059,42071438) （61972059,42071438）
江苏省自然科学基金资助项目(BK20191474,20221403) （BK20191474,20221403）
