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Updated: Jan 25, 2026

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HR-SemNet: A High-Resolution Network for Enhanced Small Object Detection With Local Contextual Semantics.

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    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 23, 2026
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    Summary
    This summary is machine-generated.

    This study introduces HR-SemNet, a novel high-resolution network for small object detection. It enhances detection accuracy by decoupling small object features from background semantics, improving performance on challenging datasets.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Object Detection

    Background:

    • High-resolution feature maps are crucial for small object detection but often lack semantic information.
    • Existing methods struggle with background smear and redundant deep features, degrading performance.
    • There is a need for improved methods to effectively capture both high-resolution object details and relevant semantic context.

    Purpose of the Study:

    • To develop a high-resolution network (HR-SemNet) that effectively detects small objects by addressing limitations in semantic information and feature representation.
    • To improve the accuracy and efficiency of small object detection systems.
    • To decouple the extraction of small object features from background contextual semantics.

    Main Methods:

    • Proposed a novel high-resolution backbone (HRB) that concentrates computational resources on high-resolution layers for clearer small object features.
    • Introduced a local context semantic module (LCSM) to extract background semantics within local windows, avoiding large-scale interference.
    • Developed HR-SemNet, which independently extracts small object semantics (HRB) and contextual semantics (LCSM).

    Main Results:

    • HR-SemNet achieved significant improvements on VisDrone, AI-TOD, and TinyPerson datasets.
    • On the VisDrone dataset, HR-SemNet improved mean average precision (mAP) by 4.6%.
    • The method reduced computational cost (GFLOPs) by 49.9% and parameter count by 94.9%.

    Conclusions:

    • HR-SemNet effectively addresses the challenges of small object detection by leveraging high-resolution features and local contextual semantics.
    • The proposed architecture offers a more efficient and accurate approach compared to traditional methods.
    • The decoupling strategy enhances the ability to detect small objects without being hindered by complex backgrounds.