论文标题
双能X射线安全图像中的联合子组件级分段和分类用于异常检测
Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery
论文作者
论文摘要
X射线行李安全筛查是广泛使用的，对于维持威胁/异常检测任务的运输安全性至关重要。近年来，使用2D X射线图像隐藏在混乱且复杂的电子/电气项目中的异常检测是近年来的主要兴趣。我们通过使用深层卷积神经网络体系结构引入联合对象子组件级细分和分类策略来解决此任务。在混乱的X射线行李安全图像的数据集上评估了该性能，该数据集由使用双能量X射线图像（伪色，高，低和有效-Z）的变体组成的消费者电气和电子项目组成。拟议的联合亚组分水平分割和分类方法实现了〜99％的真实阳性，而对异常检测任务的假阳性约为5％。
X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items, using 2D X-ray imagery is of primary interest in recent years. We address this task by introducing joint object sub-component level segmentation and classification strategy using deep Convolution Neural Network architecture. The performance is evaluated over a dataset of cluttered X-ray baggage security imagery, consisting of consumer electrical and electronics items using variants of dual-energy X-ray imagery (pseudo-colour, high, low, and effective-Z). The proposed joint sub-component level segmentation and classification approach achieve ~99% true positive and ~5% false positive for anomaly detection task.
