MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection
This work addresses the challenge of anti-overlapping object detection in X-ray security screening, which is crucial for identifying prohibited items, but it is incremental as it builds upon existing DETR-based methods with a novel loss mechanism.
The paper tackled the problem of detecting overlapping objects in X-ray images, where depth-induced superimposition complicates feature disentanglement, by introducing a multi-class min-margin contrastive learning (MMCL) framework to correct content query distributions, achieving state-of-the-art performance on three X-ray prohibited-item detection datasets.
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.