云南民族大学数学与计算机科学学院;
针对遥感图像目标排列紧密、背景复杂和小目标众多导致检测精度低的问题，提出了一种基于改进实时检测Transformer(Real-Time Detection Transformer, RT-DETR)的遥感图像检测算法。将Mosaic9数据增强应用到遥感数据中，丰富训练数据中场景和目标的组合，增强模型对不同环境下目标的识别能力。在主干网络中添加卷积块注意力模块(Convolutional Block Attention Module, CBAM),增强复杂背景下目标的关注度和图像特征提取能力，在模型中额外添加一个针对小目标的检测层，使小目标的细节特征更加突出，提升模型对小目标的检测能力。在DSTD舰船遥感数据集和NWPU VHR-10多类别遥感数据集上的实验结果显示，改进后的算法在交并比(Intersection over Union, IoU)阈值为0.5时，平均精度均值(mean Average Precision, mAP)分别达到了94.9%和94.5%,较原始RT-DETR算法分别提升了1%和1.3%,体现了改进算法在遥感图像检测上的有效性和通用性。
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| 下载次数 | 被引频次 | 阅读次数 |
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基本信息:
DOI：
中图分类号:TP751
引用信息:
[1]白金燕,江涛,魏玉梅等.基于改进RT-DETR的遥感图像检测算法[J].无线电工程,2025,55(02):334-342.
基金信息:
国家自然科学基金(61363022)~~