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English(EN) ScopeMamba-YOLO: Widening the Perceptual Scope Inward and Outward for Small Object Detection in Remote Sensing Imagery

ScopeMamba-YOLO通过新颖的上下文建模增强小目标检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种用于遥感影像中小目标检测的新方法ScopeMamba-YOLO。该方法采用一种离线选择性扫描原理，增强了模型捕捉精细细节和更广泛上下文信息的能力。关键组成部分包括级联全局上下文模块（Cascaded Global-Context Module）和选择性扫描PAN（Selective-Scan PAN），它们协同工作，在不干扰局部线索的情况下改进特征提取和上下文建模。实验表明性能显著提升，ScopeMamba-S在VisDrone-2019数据集上比YOLOv8s的mAP50提高了10.8个百分点，同时使用的参数量大大减少。 AI

影响 提高了专业影像中目标检测的准确性和效率，可能惠及监控和测绘等应用。

排序理由 该条目是一篇研究论文，详细介绍了一种新的目标检测模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ScopeMamba-YOLO通过新颖的上下文建模增强小目标检测

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该条目是一篇研究论文，详细介绍了一种新的目标检测模型。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Junjie Fan, Yijun Mai, Linduo Wei, Jiayu Rao, Junmin Bao, Qiushi Jin, Guijia Li, Yong Qi ·

    ScopeMamba-YOLO：拓宽遥感影像中小目标检测的内外感知范围

    arXiv:2609.10156v1 Announce Type: new Abstract: Small object detection in unmanned aerial vehicle (UAV) and remote sensing imagery requires preserving high-resolution detail while modeling long-range context. Adding a stride-4 detection level and removing the stride-32 stage bene…

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