Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea.
Welfare and Medical ICT Research Department, Electronics and Telecommunications Research Institute, Daejeon, South Korea.
PLoS One. 2022 Sep 1;17(9):e0272961. doi: 10.1371/journal.pone.0272961. eCollection 2022.
Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured. We propose an object detection method for efficiently detecting contraband items in both cargo and baggage for X-ray security scans. The proposed network, MFA-net, consists of three plug-and-play modules, including the multiscale dilated convolutional module, fusion feature pyramid network, and auxiliary point detection head. First, the multiscale dilated convolutional module converts the standard convolution of the detector backbone to a conditional convolution by aggregating the features from multiple dilated convolutions using dynamic feature selection to overcome the object-scale variant issue. Second, the fusion feature pyramid network combines the proposed attention and fusion modules to enhance multiscale object recognition and alleviate the object and occlusion problem. Third, the auxiliary point detection head adopts an auxiliary head to predict the new keypoints of the bounding box to emphasize the localizability without requiring further ground-truth information. We tested the performance of the MFA-net on two large-scale X-ray security image datasets from different domains: a Security Inspection X-ray (SIXray) dataset in the baggage domain and our dataset, named CargoX, in the cargo domain. Moreover, MFA-net outperformed state-of-the-art object detectors in both domains. Thus, adopting the proposed modules can further increase the detection capability of the current object detectors on X-ray security images.
深度卷积网络已被开发出来，用于在运输安全系统中的 X 射线筛查系统中自动检测违禁物品。据我们所知，现有的框架是为了仅使用行李安全 X 射线扫描来识别威胁而开发的。因此，不能保证在安全 X 射线扫描的其他领域（如货物 X 射线扫描）中的检测准确性。我们提出了一种用于在 X 射线安全扫描中有效检测货物和行李中违禁物品的目标检测方法。所提出的网络 MFA-net 由三个即插即用的模块组成，包括多尺度扩张卷积模块、融合特征金字塔网络和辅助点检测头。首先，多尺度扩张卷积模块通过使用动态特征选择从多个扩张卷积中聚合特征，将检测器骨干网络的标准卷积转换为条件卷积，以克服目标尺度变化的问题。其次，融合特征金字塔网络结合了所提出的注意力和融合模块，以增强多尺度目标识别，并减轻对象和遮挡问题。最后，辅助点检测头采用辅助头来预测边界框的新关键点，强调可定位性，而不需要进一步的地面真实信息。我们在两个来自不同领域的大规模 X 射线安全图像数据集上测试了 MFA-net 的性能：行李领域的安全检查 X 射线（SIXray）数据集和我们的货物领域数据集 CargoX。此外，MFA-net 在两个领域都优于最先进的目标检测器。因此，采用所提出的模块可以进一步提高当前目标检测器在 X 射线安全图像上的检测能力。
