Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification
Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification
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DOI：
10.1002/nme.6927
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发表时间：
2021-10
影响因子：
2.9
通讯作者：
Ben A. Wilson;P. Ledger;W. Lionheart
中科院分区：
文献类型：
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作者：
Ben A. Wilson;P. Ledger;W. Lionheart
通过改进金属探测，早期发现枪支和刀具等恐怖威胁物体，有可能减少袭击次数，改善公共安全。为了实现这一目标，利用金属探测器施加和测量的场来区分不同形状和不同金属具有相当大的潜力，因为隐藏在场扰动中的是物体特征信息。磁极化张量（MPT）提供了金属物体的经济表征，其光谱特征提供了额外的物体表征信息。MPT频谱特征可以通过测量隐藏物体的金属信号在一定频率范围内的感应电压来确定。考虑到分类，它也可以提前计算不同的威胁和非威胁对象。在本文中，我们评估了概率和非概率机器学习算法的性能，这些算法使用计算的MPT光谱特征字典进行训练，用于对金属检测的物体进行分类。我们讨论了使用合适的特征和选择合适的算法的重要性，这取决于所要解决的分类问题，我们给出了一系列实际驱动的金属探测分类问题的数值结果。
The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal detector to discriminate between different shapes and different metals since, hidden within the field perturbation, is object characterization information. The magnetic polarizability tensor (MPT) offers an economical characterization of metallic objects and its spectral signature provides additional object characterization information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of frequencies in a metal signature for a hidden object. With classification in mind, it can also be computed in advance for different threat and non‐threat objects. In this article, we evaluate the performance of probabilistic and non‐probabilistic machine learning algorithms, trained using a dictionary of computed MPT spectral signatures, to classify objects for metal detection. We discuss the importance of using appropriate features and selecting an appropriate algorithm depending on the classification problem being solved, and we present numerical results for a range of practically motivated metal detection classification problems.