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<p>Alumina ceramics used in ballistic armor applications, along with composite materials like Kevlar fiber, are crucial for the protection and operational readiness of civilian and military personnel. Accurate detection and identification of these materials can significantly enhance situational awareness and threat assessment. This study presents an advanced optical characterization approach using laser-induced fluorescence (LIF) combined with hyperspectral imaging to discriminate between protective materials. Samples were stimulated with a 390&#xa0;nm UV laser, and their standard (PL) emissions were captured using a SOC710 hyperspectral camera. Alumina ceramic and Kevlar fiber exhibited characteristic emission peaks around 702&#xa0;nm and 555&#xa0;nm, respectively. A key element of the proposed methodology is the integration of histogram-based statistical analysis to quantify pixel intensity distributions across the PL spectra. Computed mean and standard deviation (SD) values were used to establish data-driven thresholds for image segmentation. Alumina ceramic showed a mean intensity of 0.044855 with SD of 0.149456, while Kevlar recorded a mean of 0.043731 and SD of 0.137492. These metrics were used to guide the threshold values (≥ 0.9 for alumina, ≥ 1.2 for Kevlar) applied during k-means clustering (k-mc), resulting in robust material classification. The histogram-informed segmentation significantly enhanced clustering performance by reducing noise influence and improving objectivity. The combined use of LIF, MA filtering, and k-mc provides a powerful, low-complexity, and high-specificity tool for remote detection and classification of ballistic protection materials, with potential for further development in field-based operational environments.</p>

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Advanced Detection of Ballistic Armored Vehicles and Bulletproof Vests Using Laser-Induced Fluorescence and Spectral Analysis

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Amr Saber,
  • Alaaeldin Mahmoud,
  • Yasser H. El-Sharkawy

摘要

Alumina ceramics used in ballistic armor applications, along with composite materials like Kevlar fiber, are crucial for the protection and operational readiness of civilian and military personnel. Accurate detection and identification of these materials can significantly enhance situational awareness and threat assessment. This study presents an advanced optical characterization approach using laser-induced fluorescence (LIF) combined with hyperspectral imaging to discriminate between protective materials. Samples were stimulated with a 390 nm UV laser, and their standard (PL) emissions were captured using a SOC710 hyperspectral camera. Alumina ceramic and Kevlar fiber exhibited characteristic emission peaks around 702 nm and 555 nm, respectively. A key element of the proposed methodology is the integration of histogram-based statistical analysis to quantify pixel intensity distributions across the PL spectra. Computed mean and standard deviation (SD) values were used to establish data-driven thresholds for image segmentation. Alumina ceramic showed a mean intensity of 0.044855 with SD of 0.149456, while Kevlar recorded a mean of 0.043731 and SD of 0.137492. These metrics were used to guide the threshold values (≥ 0.9 for alumina, ≥ 1.2 for Kevlar) applied during k-means clustering (k-mc), resulting in robust material classification. The histogram-informed segmentation significantly enhanced clustering performance by reducing noise influence and improving objectivity. The combined use of LIF, MA filtering, and k-mc provides a powerful, low-complexity, and high-specificity tool for remote detection and classification of ballistic protection materials, with potential for further development in field-based operational environments.