Sunday 16 March 2025
The quest for better security screening has led researchers to develop innovative solutions, and a new approach promises improved detection of prohibited items in X-ray images. The technique, dubbed Category Semantic Prior Contrastive Learning (CSPCL), enhances the performance of Deformable DETR-based models by aligning class prototypes with content queries.
The problem of detecting prohibited items in X-ray images is a complex one, as it requires distinguishing between objects that may be hidden or obscured by clutter. Traditional methods often struggle with overlapping features, leading to inaccurate results. CSPCL addresses this issue by introducing a novel mechanism that supplements and clarifies semantic information within the content queries.
The key innovation lies in the design of a specific contrastive loss function, which includes Intra-Class Truncated Attraction (ITA) loss and Inter-Class Adaptive Repulsion (IAR) loss. ITA loss attracts intra-class category-specific content queries to class prototypes, while IAR loss adaptsively repels inter-class queries based on similarity between class prototypes.
The CSPCL mechanism is easy to integrate into Deformable DETR-based models, making it a plug-and-play solution for existing frameworks. Extensive experiments on two datasets, PIXray and OPIXray, demonstrate the effectiveness of CSPCL in enhancing detection performance without increasing model complexity.
Visualization of deformable attention sampling points, reference points, and prediction results reveals that CSPCL improves feature extraction by focusing on foreground information. In cases where background noise is severe, CSPCL-based models are better at distinguishing between objects and reducing false positives.
The impact of CSPCL extends beyond X-ray security screening, as it can be applied to other object detection tasks with overlapping features. Its ability to adaptively adjust the representation space based on class prototypes makes it a versatile tool for various applications.
In summary, Category Semantic Prior Contrastive Learning is an innovative approach that addresses the challenges of detecting prohibited items in X-ray images by aligning class prototypes with content queries. Its effectiveness has been demonstrated through extensive experiments and visualizations, making it a promising solution for improving security screening efficiency.
Cite this article: “Enhancing X-Ray Security Screening Through Category Semantic Prior Contrastive Learning”, The Science Archive, 2025.
X-Ray Images, Security Screening, Object Detection, Contrastive Learning, Deformable Detr, Category Semantic Prior, Prohibited Items, False Positives, Feature Extraction, Image Classification
