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Real-time localization and classification of the fast-moving target based on complementary single-pixel detection.

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    This study introduces an image-free method for real-time object localization and classification using single-pixel detection. The novel approach achieves high accuracy and speed, overcoming limitations of traditional imaging techniques.

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    Area of Science:

    • Optoelectronics
    • Computational Imaging
    • Pattern Recognition

    Background:

    • Traditional imaging struggles with real-time localization and classification of fast objects due to data needs, slow rates, and motion blur.
    • Non-visible wavelength imaging presents additional challenges for dynamic object analysis.

    Purpose of the Study:

    • To develop an image-free method for simultaneous real-time target localization and classification.
    • To integrate target localization and classification into a unified framework using complementary single-pixel detection.

    Main Methods:

    • Utilized complementary single-pixel detection with four specific illumination patterns.
    • Employed centralized geometric moments for target localization and classification.
    • Achieved simultaneous determination of centroid position and target shape.

    Main Results:

    • The proposed method achieved an update rate of up to 5.55 kHz.
    • Experimental results showed root-mean-square error (RMSE) for centroid localization below 0.5 pixels.
    • Achieved 93.3% classification accuracy for 30 different objects under diverse conditions.

    Conclusions:

    • The image-free method offers robust and accurate real-time localization and classification of fast-moving objects.
    • Demonstrated strong adaptability in complicated environments, outperforming traditional imaging.
    • Potential applications include target tracking, character recognition, industrial automation, and optoelectronic neural networks.