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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Improved YOLOX detection algorithm for contraband in X-ray images.

Yinsheng Zhang, Wenxiao Xu, Shanshan Yang

    Applied Optics
    |October 18, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an advanced computer vision framework for automatic contraband detection, significantly improving accuracy in X-ray security screening. The new method enhances safety by reducing detection errors in public places.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Security Technology

    Background:

    • Current contraband detection relies on manual X-ray image analysis by security inspectors.
    • Manual inspection is prone to errors like missed or wrong detections, especially when contraband is concealed.
    • This poses risks to public safety and property in high-intensity environments.

    Purpose of the Study:

    • To develop an automated contraband detection framework using computer vision.
    • To enhance the accuracy and reliability of security screening processes.
    • To address the limitations of manual inspection in detecting hidden contraband.

    Main Methods:

    • The proposed framework is built upon the YOLOX object detection network.
    • Key improvements include feature fusion, a double attention mechanism, and modified classification regression loss.
    • The model was trained and validated on the public safety SIXray dataset.

    Main Results:

    • The enhanced YOLOX model demonstrated superior performance compared to the benchmark YOLOX-S.
    • Achieved a 5.0% improvement in average accuracy on the SIXray dataset.
    • Indicates a significant advancement in automated contraband detection capabilities.

    Conclusions:

    • The developed computer vision framework offers a more accurate and reliable method for contraband detection.
    • This technology paves the way for large-scale, automated security screening in public areas.
    • Improved detection accuracy enhances overall public safety and security.