MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection

TL;DR

This paper introduces MMCL, a contrastive learning framework that improves X-ray object detection by balancing content query distributions, leading to better separation of overlapping objects and state-of-the-art results.

Contribution

The paper proposes a novel multi-class min-margin contrastive learning method to correct content query distributions for enhanced anti-overlapping object detection in X-ray images.

Findings

MMCL improves detection accuracy on three X-ray datasets.

The method achieves state-of-the-art performance across multiple backbone networks.

Enhanced intra-class diversity and inter-class separability are demonstrated.

Abstract

Unlike natural images with occlusion-based overlap, X-ray images exhibit depth-induced superimposition and semi-transparent appearances, where objects at different depths overlap and their features blend together. These characteristics demand specialized mechanisms to disentangle mixed representations between target objects (e.g., prohibited items) and irrelevant backgrounds. While recent studies have explored adapting detection transformers (DETR) for anti-overlapping object detection, the importance of well-distributed content queries that represent object hypotheses remains underexplored. In this paper, we introduce a multi-class min-margin contrastive learning (MMCL) framework to correct the distribution of content queries, achieving balanced intra-class diversity and inter-class separability. The framework first groups content queries by object category and then applies two proposed complementary loss components: a multi-class exclusion loss to enhance inter-class separability, and a min-margin clustering loss to encourage intra-class diversity. We evaluate the proposed method on three widely used X-ray prohibited-item detection datasets, PIXray, OPIXray, and PIDray, using two backbone networks and four DETR variants. Experimental results demonstrate that MMCL effectively enhances anti-overlapping object detection and achieves state-of-the-art performance on both datasets. Code will be made publicly available on GitHub.