Enhancing Prohibited Item Detection through X-ray-Specific Augmentation and Contextual Feature Integration

Abstract: X-ray prohibited item detection faces challenges due to the long-tail distribution and unique characteristics of X-ray imaging. Traditional data augmentation strategies, such as copy-paste and mixup, are ineffective at improving the detection of rare items due to the complex interactions between overlapping objects. Furthermore, X-ray imaging removes easily distinguishable features like color and texture, making it difficult to differentiate between visually similar categories. To address these challenges, in this work, we propose the X-ray Imaging-driven Detection Network (XIDNet). Inspired by the unique characteristics of X-ray imaging, this network introduces two key innovations: a novel X-ray-specific augmentation strategy that generates more realistic training samples for rare items, thereby improving detection performance for categories with insufficient samples, and an contextual feature integration algorithm that captures the spatial and semantic interactions between objects and surroundings under X-ray imaging, enhancing the model's ability to distinguish between similar categories. Extensive experimental results show that XIDNet effectively leverages X-ray imaging characteristics to significantly improve detection performance, outperforming popular SoTA methods by up to 17.2% in tail categories.