Automated Segmentation of Prohibited Items in X-Ray Baggage Images Using Dense De-Overlap Attention Snake

Prohibited item segmentation has a wide range of applications in the security check field, such as computer-aided screening, threat image projection and material discrimination. However, the severe object overlapping in X-ray baggage images restricts the performance of common CNN-based segmentation methods greatly. Worse, no public dataset can be used to promote research in this challenging and promising area. In this paper, to cope with these problems, we present the first Prohibited Item X-ray segmentation dataset named PIXray. PIXray comprises 5,046 X-ray images, in which 15 classes of 15,201 prohibited items are annotated as instance-level masks. Besides, we contribute a dense de-overlap attention snake (DDoAS) in the context of deep learning for automated and real-time prohibited item segmentation. DDoAS mainly includes a dense de-overlap module (DDoM) and an attention deforming module (ADM). Specifically, DDoM is designed to infer prohibited item information accurately from extreme background overlaps through dense reversed connections. ADM aims to improve the low learning efficiency introduced by large variations in shapes and sizes among different prohibited items. Comprehensive evaluation on the PIXray shows the effectiveness and superiority of DDoM and ADM. DDoM excels at recognizing prohibited items from complex backgrounds than other in-domain methods and achieves consistent performance gain over various network backbones, extending the idea of tackling overlapping images data. ADM can ease the model training and further refine the mask quality. Furthermore, out-of-domain experiments prove that DDoAS can also be applied to natural images and achieves comparable performance to the state-of-the-art methods, which implies its potential applications in other fields. The dataset and source code are available athttps://github.com/Mbwslib/DDoAS.