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Related Experiment Video

Updated: May 27, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Robust Fine-Grained Oriented Ship Detection for Remote Sensing imagery via Controllable Generative Pretraining.

Da He, Haoran Liu, Zeyu Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 25, 2026
    PubMed
    Summary
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    This study introduces a new dataset and framework for fine-grained ship recognition. The approach improves ship detection accuracy in complex maritime conditions, enhancing maritime surveillance capabilities.

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Maritime Surveillance

    Background:

    • Fine-grained ship recognition is crucial for maritime applications but faces challenges with limited datasets and complex environmental conditions.
    • Existing ship detection datasets lack granularity, and arbitrary ship orientations/distributions in complex maritime settings hinder performance.

    Purpose of the Study:

    • To address limitations in ship detection datasets and complex maritime conditions.
    • To develop a robust framework for fine-grained ship recognition in remote sensing imagery.

    Main Methods:

    • Annotated a large-scale fine-grained ship instance detection dataset (LAFI) with 49 categories.
    • Proposed a controllable generative knowledge-driven ship detection framework (COSD) using synthetic data generation.

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    Last Updated: May 27, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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  • Designed a heterogeneous feature alignment decoder to handle diverse ship orientations and distributions.

    • Main Results:

    • The COSD framework achieved significant improvements in mean average precision (mAP) over state-of-the-art (SOTA) methods.
    • Demonstrated superior performance in detecting small, densely packed, and arbitrarily oriented ships.
    • The LAFI dataset provides a valuable resource for advancing fine-grained ship recognition.

    Conclusions:

    • The proposed COSD framework and LAFI dataset effectively address key challenges in fine-grained ship recognition.
    • This work advances the accuracy and robustness of ship detection in complex maritime environments.
    • The findings have significant implications for maritime security and monitoring.