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<p>In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection, rapid measurements using a NaI(Tl) detector often result in low photon counts, weak characteristic peaks, and significant statistical fluctuations. These issues can lead to potential failures in peak-searching-based identification methods. To address the low precision associated with short-duration measurements of radionuclides, this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model. Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra. With only 10% of target domain samples used for training, the accuracy on real low-count spectral samples was 95.56%. This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples. The proposed method also exhibits strong generalization capabilities, effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.</p>

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Identification algorithm of low-count energy spectra under short-duration measurement based on heterogeneous sample transfer

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Hao-Lin Liu,
  • Hai-Bo Ji,
  • Jiang-Mei Zhang,
  • Jing Lu

摘要

In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection, rapid measurements using a NaI(Tl) detector often result in low photon counts, weak characteristic peaks, and significant statistical fluctuations. These issues can lead to potential failures in peak-searching-based identification methods. To address the low precision associated with short-duration measurements of radionuclides, this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model. Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra. With only 10% of target domain samples used for training, the accuracy on real low-count spectral samples was 95.56%. This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples. The proposed method also exhibits strong generalization capabilities, effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.