English(EN) When Does Sliced Inference Pay Off? Resolution, Tiling and Dense Fusion for Small Object Detection

小目标检测的切片推理：分辨率 vs. 切片

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 16:25

一篇新论文探讨了切片推理技术在检测高分辨率图像中的小目标时的有效性。研究人员发现，在计算资源有限的情况下，简单地增加单次推理的分辨率可以优于带有框融合的切片方法。他们还引入了 SCAHI（覆盖感知超推理切片），这是一种能更好地利用切片输出的互补信息的方法，在 VisDrone 和 DOTA 数据集上，对于各种 YOLO 和 FCOS 检测器，其性能优于传统的框融合。 AI

影响引入了 SCAHI，一种新颖的小目标检测方法，通过更好地利用切片推理输出来改进现有技术。

排序理由该集群包含一篇详细介绍目标检测新方法和实验结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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本文如何被排名

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-11-09 16:25

切片推理何时能带来收益？小目标检测中的分辨率、切片和密集融合

Sliced inference such as SAHI is the standard remedy for small objects in high-resolution imagery, but its gains are usually measured against low-resolution baselines. We revisit sliced inference with scale as the explicit variable and ask when tiling actually pays off. We compar…

本文如何被排名

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切片推理何时能带来收益？小目标检测中的分辨率、切片和密集融合

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