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Sliced inference for small object detection: Resolution vs. Tiling

By PulseAugur Editorial · [1 sources] ·

A new paper explores the effectiveness of sliced inference techniques, commonly used for detecting small objects in high-resolution images. Researchers found that simply increasing the resolution of a single inference pass can outperform tiling with box fusion, especially when compute resources are a limiting factor. They also introduced SCAHI (Slicing with Coverage-Aware Hyper Inference), a method that better utilizes the complementary information from tiled outputs, leading to improved performance over traditional box fusion for various YOLO and FCOS detectors on VisDrone and DOTA datasets. AI

IMPACT Introduces SCAHI, a novel method for small object detection that improves upon existing techniques by better utilizing tiled inference outputs.

RANK_REASON The cluster contains a research paper detailing a new method and experimental findings in object detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Sliced inference for small object detection: Resolution vs. Tiling

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The cluster contains a research paper detailing a new method and experimental findings in object detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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…

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