  Prohibited Item detection in X-ray images is one of the most effective
security inspection methods.However, differing from natural light images, the
unique overlapping phenomena in X-ray images lead to the coupling of foreground
and background features, thereby lowering the accuracy of general object
detectors.Therefore, we propose a Multi-Class Min-Margin Contrastive Learning
(MMCL) method that, by clarifying the category semantic information of content
queries under the deformable DETR architecture, aids the model in extracting
specific category foreground information from coupled features.Specifically,
after grouping content queries by the number of categories, we employ the
Multi-Class Inter-Class Exclusion (MIE) loss to push apart content queries from
different groups. Concurrently, the Intra-Class Min-Margin Clustering (IMC)
loss is utilized to attract content queries within the same group, while
ensuring the preservation of necessary disparity. As training, the inherent
Hungarian matching of the model progressively strengthens the alignment between
each group of queries and the semantic features of their corresponding category
of objects. This evolving coherence ensures a deep-seated grasp of category
characteristics, consequently bolstering the anti-overlapping detection
capabilities of models.MMCL is versatile and can be easily plugged into any
deformable DETR-based model with dozens of lines of code. Extensive experiments
on the PIXray and OPIXray datasets demonstrate that MMCL significantly enhances
the performance of various state-of-the-art models without increasing
complexity. The code has been released at
https://github.com/anonymity0403/MMCL.
