ABTD-Net: Autonomous Baggage Threat Detection Networks for X-ray Images

Abstract

Automated security screening has a significant role In protecting public spaces from security threats by employing X-ray images to detect prohibited items. However, there are challenges of noise production due to squeezing, occlusion, and penetration of luggage objects. Additionally, the hues of objects are monotonous and lack luster. To solve these problems, we propose an Autonomous Baggage Threat Detection Network (ABTD-Net) for accurate prohibited item detection. To tackle the difficulty of capturing distinctive visual features, we constructed a Feature Adjustment Head (FAH) to refine pyramid features. Specifically, we designed an Attention Module (AM) at several places after initially using a Dense Unidirectional Propagation (DUP) to filter noise. Furthermore, we created a Feature Fusion Head (FFH) that dynamically fuses hierarchical visual information under object occlusion, including early-fusion and late-fusion. Extensive experiments on security inspection X-ray datasets OPIXray and HiXray demonstrate the superiority of our proposed method.