论文标题
DCNNS：双能X射线行李图像的全武器家庭威胁检测的转移学习比较
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery
论文作者
论文摘要
卷积神经网络的最新进步在图像识别任务中提高了超人的性能水平[13，25]；但是，随着每年越来越多的包裹越来越多，威胁的分类成为英国边界平稳运行不可或缺的一部分。在这项工作中，我们提出了第一个管道，以有效处理双能X射线扫描仪输出，并执行能够区分枪支家族（突击步枪，左轮手枪，弹手枪，shot弹枪和枪枪枪）的分类。通过此管道，我们将重分卷积神经网络体系结构与X射线行李域进行比较，并通过转移学习并显示Resnet50最适合分类 - 概述了在该域内进行操作成功的许多考虑因素。
Recent advancements in Convolutional Neural Networks have yielded super-human levels of performance in image recognition tasks [13, 25]; however, with increasing volumes of parcels crossing UK borders each year, classification of threats becomes integral to the smooth operation of UK borders. In this work we propose the first pipeline to effectively process Dual-Energy X-Ray scanner output, and perform classification capable of distinguishing between firearm families (Assault Rifle, Revolver, Self-Loading Pistol,Shotgun, and Sub-Machine Gun) from this output. With this pipeline we compare re-cent Convolutional Neural Network architectures against the X-Ray baggage domain via Transfer Learning and show ResNet50 to be most suitable to classification - outlining a number of considerations for operational success within the domain.
