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Updated: Nov 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Concealed Object Detection.

Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 1, 2021
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    Summary
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    This study introduces concealed object detection (COD), a challenging task of identifying visually embedded objects. A new dataset, COD10K, and a strong baseline model, SINet, were developed to advance this field.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Concealed object detection (COD) is a challenging computer vision task due to high similarities between objects and backgrounds.
    • Existing object detection and segmentation methods struggle with visually embedded objects.

    Purpose of the Study:

    • To systematically study concealed object detection (COD).
    • To introduce a large-scale dataset (COD10K) and a novel baseline model (SINet) for COD research.

    Main Methods:

    • Collected and annotated the COD10K dataset with 10,000 images, 78 object categories, and rich annotations.
    • Designed the Search Identification Network (SINet), a robust baseline model inspired by animal hunting strategies.

    Main Results:

    • SINet demonstrated superior performance compared to twelve state-of-the-art methods on various datasets.
    • The COD10K dataset provides comprehensive resources for advancing concealed object understanding.

    Conclusions:

    • The study establishes a strong foundation for concealed object detection research.
    • SINet and COD10K are expected to catalyze future advancements in COD and related vision tasks.