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
this https URL

禁止物品在 X 射线图像中的检测是一种最有效的安全检查方法，然而，与自然光图像不同，X 射线图像中的独特重叠现象导致前景和背景特征的耦合，从而降低了一般物体检测器的准确性。因此，我们提出了一种多类最小间隔对比学习（MMCL）方法，在可变形 DETR 体系结构下明确内容查询的类别语义信息，帮助模型从耦合特征中提取特定类别的前景信息。