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English(EN) SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks

SFN-YOLO模型通过感知尺度融合增强家禽检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了SFN-YOLO，这是一种用于在自由放养环境中检测和定位家禽的新方法。该方法采用感知尺度融合来整合局部和全局特征，从而在复杂的背景和不同的目标大小下提高检测精度。附带的M-SCOPE数据集和SFN-YOLO模型表现强劲，在参数量远少于现有基准的情况下实现了80.7%的mAP，从而能够应用于智能农业系统的实时检测。 AI

影响 通过增强的目标检测能力，提高农业的效率和自动化水平。

排序理由 发表了一篇详细介绍新计算机视觉模型和数据集的学术论文。[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 →

SFN-YOLO模型通过感知尺度融合增强家禽检测

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发表了一篇详细介绍新计算机视觉模型和数据集的学术论文。[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) · Jie Chen, Yuhong Feng, Tao Dai, Hao Wang, Hongtao Chen, Zhaoxi He, Mingzhe Liu, Jiancong Bai ·

    SFN-YOLO：迈向通过感知尺度融合网络实现自由放养禽类检测

    arXiv:2509.17086v2 Announce Type: replace Abstract: Detecting and localizing poultry is essential for advancing smart poultry farming. Despite the progress of detection-centric methods, challenges persist in free-range settings due to multiscale targets, obstructions, and complex…

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