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Multi-scale pyramid convolution transformer for remote-sensing object detection

delete2025-07-06
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PRE
AI
J
Jin Huagang
Y
Yu Zhou
DOI：10.1016/j.imavis.2025.105651delete
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摘要

摘要

En 中文
• 针对RSOD问题提出了一种新颖的基于金字塔卷积Transformer的框架，该框架在每个编码器块中使用双路径分支来增强多尺度目标的基本归纳偏差，从而提升判别特征表示能力。• 为应对大规模变化，设计了双向特征聚合来促进高层语义特征与低层细节信息的深度交互，增强了网络的多尺度目标表示能力。• 为降低计算成本，通过调整编码器-解码器连接关系引入了一种稀疏采样策略，该策略在先验检测结果指导下迭代识别感兴趣区域，然后在每个稀疏关键点提取多尺度特征。• 进行了全面的实验以验证所提MPCViT的有效性。在NWPU VHR-10和DIOR数据集上，性能提升分别达到1.29%和2.13%，显著优于当前最优的ARS-DETR方法。

期刊

Image and Vision Computing 封面图
Image and Vision Computing
IF:
4.2
论文数:
4.1K
被引数:
6.7K

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引用论文

引用论文

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