论文标题
可训练的结构张量，用于在极端遮挡下检测自动行李威胁
Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion
论文作者
论文摘要
即使对于专家人员来说，发现行李威胁是最艰巨的任务之一。许多研究人员开发了计算机辅助筛选系统，以从行李X射线扫描中认识到这些威胁。但是，所有这些框架在识别极端遮挡下的违禁品中都受到限制。本文提出了一个新的实例分割框架，该框架利用可训练的结构张量来突出显示被遮挡和混乱的违禁品（通过扫描多个主要方向）的轮廓，同时抑制了无关紧要的包装含量。所提出的框架已在四个公开可用的X射线数据集上进行了广泛的测试，在该数据集中，它以平均平均精度得分的范围优于最新框架。此外，据我们所知，这是唯一在从四种不同类型的X射线扫描仪获得的灰度和彩色扫描中验证的唯一框架。
Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are limited in identifying the contraband items under extreme occlusion. This paper presents a novel instance segmentation framework that utilizes trainable structure tensors to highlight the contours of the occluded and cluttered contraband items (by scanning multiple predominant orientations), while simultaneously suppressing the irrelevant baggage content. The proposed framework has been extensively tested on four publicly available X-ray datasets where it outperforms the state-of-the-art frameworks in terms of mean average precision scores. Furthermore, to the best of our knowledge, it is the only framework that has been validated on combined grayscale and colored scans obtained from four different types of X-ray scanners.
