BGM: Background Mixup for X-ray Prohibited Items Detection

TL;DR

This paper introduces Background Mixup (BGM), a novel background-based data augmentation technique for X-ray prohibited items detection that improves model robustness by leveraging background cues and material properties.

Contribution

BGM is a new augmentation method that mixes background patches based on physical properties of X-ray images, enhancing detection performance without extra annotations.

Findings

BGM consistently outperforms strong baselines on multiple benchmarks.

BGM is compatible with existing augmentation techniques, boosting their effectiveness.

The method is lightweight and easy to integrate into current detection pipelines.

Abstract

Current data-driven approaches for X-ray prohibited items detection remain under-explored, particularly in the design of effective data augmentations. Existing natural image augmentations for reflected light imaging neglect the data characteristics of X-ray security images. Moreover, prior X-ray augmentation methods have predominantly focused on foreground prohibited items, overlooking informative background cues. In this paper, we propose Background Mixup (BGM), a background-based augmentation technique tailored for X-ray security imaging domain. Unlike conventional methods, BGM is founded on an in-depth analysis of physical properties including: 1) X-ray Transmission Imagery: Transmitted X-ray pixels represent composite information from multiple materials along the imaging path. 2) Material-based Pseudo-coloring: Pseudo-coloring in X-ray images correlates directly with material properties, aiding in material distinction. Building upon the above insights, BGM mixes background patches across regions on both 1) texture structure and 2) material variation, to benefit models from complicated background cues. This enhances the model's capability to handle domain-specific challenges such as occlusion-induced discriminative imbalance. Importantly, BGM is orthogonal and fully compatible with existing foreground-focused augmentation techniques, enabling joint use to further enhance detection performance. Extensive experiments on multiple X-ray security benchmarks show that BGM consistently surpasses strong baselines, without additional annotations or significant training overhead. This work pioneers the exploration of background-aware augmentation in X-ray prohibited items detection and provides a lightweight, plug-and-play solution with broad applicability.