论文标题
从X射线扫描中牢固地识别严重遮挡的行李物品的级联结构张量框架框架
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
论文作者
论文摘要
在过去的二十年中，行李扫描在全球范围内已成为主要航空安全问题之一。手动筛选行李物品是乏味，容易出错的和妥协的隐私。因此，许多研究人员开发了基于X射线图像的自主系统来解决这些缺陷。本文提出了一个级联的结构张量框架，可以自动提取和识别出严重塞和混乱的行李中的可疑物品。所提出的框架是独一无二的，因为它通过迭代地从不同方向选择基于轮廓的过渡信息来智能提取每个对象，并且仅使用单个馈送前向前的卷积神经网络进行识别。该提议的框架已通过公开可用的GDXRay和Six Sare数据集进行了严格的评估，该框架总共扫描了1,067,381次X射线扫描，在该数据集中，它在GDXRARE上的平均平均精度得分平均为0.9343，在GDXRARE上的平均平均精度分数超过了最先进的解决方案，并且在六乘上识别了0.9595，以识别高度贴合和高度贴合的项目。此外，与现有的基于公共可用对象检测器的现有解决方案相比，所提出的计算框架在计算上实现了4.76 \％的运行时性能
In the last two decades, baggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is tedious, error-prone, and compromise privacy. Hence, many researchers have developed X-ray imagery-based autonomous systems to address these shortcomings. This paper presents a cascaded structure tensor framework that can automatically extract and recognize suspicious items in heavily occluded and cluttered baggage. The proposed framework is unique, as it intelligently extracts each object by iteratively picking contour-based transitional information from different orientations and uses only a single feed-forward convolutional neural network for the recognition. The proposed framework has been rigorously evaluated using a total of 1,067,381 X-ray scans from publicly available GDXray and SIXray datasets where it outperformed the state-of-the-art solutions by achieving the mean average precision score of 0.9343 on GDXray and 0.9595 on SIXray for recognizing the highly cluttered and overlapping suspicious items. Furthermore, the proposed framework computationally achieves 4.76\% superior run-time performance as compared to the existing solutions based on publicly available object detectors
