多路径注意力和规模感知融合,用于在遥感图像中准确检测物体
Jing Liu1, Junjie Tao2, Xiaoyong Liu2
1Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, School of Computer Science, Xijing University, Xi'an, 710123, China. 20180075@xijing.edu.cn.
Scientific reports
|November 25, 2025
概括
超融合-DEIM增强了远程传感中的物体检测,提高了小目标的准确性. 这种新的级联检测范式提供了强大的性能和实时功能,对于智能解释系统至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 在遥感中精确的物体检测对于智能系统至关重要.
- 现有的方法在特征表示,语义建模和多尺度融合方面扎,特别是对于小目标.
- 在当前的检测模型中,小物体容易被遗漏和错误分类.
研究的目的:
- 介绍HyperFusion-DEIM,一个新的级联检测范式.
- 为了增强对象级表示,上下文语义依赖性和规模感知特征集成.
- 解决目前探测器在遥感图像中识别小规模目标方面的局限性.
主要方法:
- 开发了多路径注意力网络 (MAPNet) 与多路径注意力融合 (MPAF) 和浅级强硬特征下采样 (SRFD) 以用于小物体识别.
- 集成了规模感知特征增强 (SAFE) 编码器与多级特征度 (MFC) 进行跨层几何对齐.
- 集成的变压器层与HyperACE,用于长距离的语义相关性,同时保持空间保真.
主要成果:
- 超融合-DEIM在SIMD基准上实现了64.5%的AP,表现优于RT-DETR和DEIM.
- 在VEDAI基准上,它超过了YOLOv12,YOLOv13,RT-DETRv2和DEIM.
- 该模型展示了实时性能,在SIMD上达到296.33 FPS,在VEDAI上达到79.7 FPS.
结论:
- 超融合-DEIM显著提高了远程传感中的对象检测准确性和稳定性.
- 拟议的模型有效地处理小规模目标和复杂的特征融合.
- 超融合-DEIM为资源有限的环境中实时检测提供了一个实用的解决方案.
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