Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects, we propose to derive proposals according to the hierarchy of the regions defined by the contours. The proposed framework is rigorously validated on three public datasets, dubbed GDXray, SIXray, and OPIXray, where it outperforms the state-of-the-art methods by achieving the mean average precision score of 0.9779, 0.9614, and 0.8396, respectively. Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.
翻译:为从X射线图像中筛选违禁物品而设计的自动化系统仍然在高度混乱、隐蔽和极端封闭的情况下面临困难。在本文件中,我们利用一个新型的多尺度轮廓分解框架来应对这一挑战,该框架在行李X射线扫描中有效识别了包装违禁物品数据。与使用基于区域或基于关键点的技术生成物体周围多盒的标准模型不同,我们提议根据轮廓所界定区域的等级顺序提出建议。在三个公共数据集(称为GDXray、SIXray和OPIXray)上,拟议框架经过严格验证,通过分别达到0.9779、0.9614和0.8396的平均精度分数,超过了最新工艺方法。此外,据我们所知,这是第一个利用多尺度信息来识别彩色和灰色X射线安全图像中染色和隐藏的违禁资料的轮廓分解框架。