Deep learning for 2D passive source detection in presence of complex cargo
Deep learning for 2D passive source detection in presence of complex cargo
复制标题
存在复杂货物时进行二维被动源检测的深度学习
DOI：
10.1088/1361-6420/abb51d
复制
发表时间：
2020
期刊：
影响因子：
2.1
通讯作者：
Ragusa, J
中科院分区：
文献类型：
--
作者：
Baines, W;Kuchment, P;Ragusa, J
高噪声环境下的源检测方法对于单光子发射计算机断层扫描医学成像非常重要，对于国土安全应用尤其重要，这是我们的主要兴趣。在后一种情况下，被动探测具有显著背景噪声（信噪比为1%或更低）的低排放核源的存在。在被动辐射问题中，需要方向敏感探测器来匹配图像和数据的维度。在标准的愤怒γ相机中用于此目的的准直不是一种选择。相反，可以利用康普顿γ相机（以及它们用于其他类型辐射的类似物）。两个作者和他们的合作者之前提出的反投影方法使得在随机均匀背景的存在下能够进行检测。然而，在大多数实际应用中，集装箱和卡车中的货物包装会产生强吸收和散射区域，同时留下一些流动间隙。在这种情况下，反投影方法证明是无效的，并失去其检测能力。尽管如此，视觉感知的反投影图片表明，一些迹象的存在来源可能仍然在数据中。为了学习这些特征（如果它们确实存在），在2D中实现了深度神经网络方法，该方法在低散射情况下确实表现出比反向投影技术更高的灵敏度和特异性，并且在复杂货物的存在使反向投影完全失败时效果良好。
Methods for source detection in high noise environments are important for single-photon emission computed tomography medical imaging and especially crucial for homeland security applications, which is our main interest. In the latter case, one deals with passively detecting the presence of low emission nuclear sources with significant background noise (with signal to noise ratio 1% or less). In passive emission problems, direction sensitive detectors are needed, to match the dimensionalities of the image and the data. Collimation, used for that purpose in standard Anger γ-cameras, is not an option. Instead, Compton γ-cameras (and their analogs for other types of radiation) can be utilized. Backprojection methods suggested before by two of the authors and their collaborators enable detection in the presence of a random uniform background. In most practical applications, however, cargo packing in shipping containers and trucks creates regions of strong absorption and scattering, while leaving some streaming gaps open. In such cases backprojection methods prove ineffective and lose their detection ability. Nonetheless, visual perception of the backprojection pictures suggested that some indications of presence of a source might still be in the data. To learn such features (if they do exist), a deep neural network approach is implemented in 2D, which indeed exhibits higher sensitivity and specificity than the backprojection techniques in a low scattering case and works well when presence of complex cargo makes backprojection fail completely.