Adaptxray: Vision Transformer And Adapter In X-Ray Images For Prohibited Items Detection

Prohibited items detection refers to the non-contact inspection of passenger baggage for potential threats through X-ray image. Since the uncertainty of artificial security screening, previous research has mainly concentrated on direct transfer by universal detection frameworks based on natural image and design in enhance or aware module with salient features like edge and color. With the increasing complexity in both categories and quantities of X-ray security inspection, the unreliability of direct transfer and complication of task-specific design make the existing algorithms difficult to reliably and efficiently adapt the complex security inspection. To address this challenge, we propose the Adapter in X-ray (AdaptXray), which firstly explores pre-trained Vision Transformer with powerful representation and Parameter Efficient Transfer Learning method applying for prohibited items detection. Specifically, we design Color Prior Extractor to perceive local prior features from different color spaces. Subsequently, we develop Global-aware Self-Adapter to adaptively perceive and optimize the global universal features in the backbone. Additionally, we propose Local-aware Interactive Adapter to incorporate prior knowledge into the pretrained backbone. Thorough experimentation on two public baggage datasets, namely OPIXray and PIDray, demonstrates that the effectiveness of our proposed method, outperforming the existing renown CNN -based detection approaches.