Electronic explosives inspection: a fine-grained X-ray benchmark and few-shot prohibited phone detection model

Jianzhao Cui, Xiongfei Li, Xiaoli Zhang, Sa Huang, Yuncong Feng

Abstract: Under the X-ray scanning, mobile phone explosive modified at the battery is stealthy, which increases the difficulty of security inspections to detect prohibited phones. This critical issue has not received enough attention. In this paper, we contribute the first modified mobile phone X-ray image benchmark for object recognition, named MPXray. MPXray focuses on the detection of prohibited items that are imbalanced and fine-grained. To deal with such anomaly detection task where few abnormal samples are obtained, we propose a few-shot prohibited phone detection (FSPPD) model based on contrastive learning. FSPPD uses an unsupervised sampling module(USM) to obtain anchors that are more representative of the data distribution, so as to construct balanced input for contrastive learning. For handling hard-to-classify caused by fine-grained samples, an anchor-wise contrastive loss(AW-CL) is designed to supervise models speed up the proximity between intra-class samples and separation between between-class samples. FSPPD is more suitable for applications where electronic products need to be checked individually. We evaluate our model on MPXray, from both the classification perspective and anomaly detection perspective. Experimental results show that our model achieves better recall for modified mobile phones. Additionally, we verify the generalization ability of the proposed model on the CIFAR10 dataset. Compared with widely used algorithms, our model achieves certain superiority in recall metrics.