This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%–12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

本文介绍了使用伽马射线光谱和现代机器学习方法进行自动放射性物质检测和识别的最先进方法的研究。最近的发展在深度学习算法中激发了这一点，并且所提出的方法提供了比当前最先进的模型更好的性能。机器学习模型，例如：全连接、循环、卷积和梯度提升决策树，应用于各种测试条件下，并讨论了它们的优缺点。此外，通过结合全连接和卷积神经网络开发了混合模型，在不同的机器学习模型中表现出最佳性能。这些改进由模型的测试性能指标（即 F1 分数）93 表示。33%，在各种条件下比最先进的模型提高了 2%–12%。实验结果表明，经典神经网络和现代深度学习架构的融合是解释需要实时和远程检测的伽马光谱数据的合适选择。