Item Detection

Item detection in X-ray images focuses on automatically identifying prohibited objects for security applications, aiming to improve efficiency and accuracy compared to manual inspection. Current research emphasizes overcoming challenges like object overlap and class imbalance using advanced architectures such as Vision Transformers, Deformable DETRs, and variations of RetinaNet, often incorporating contrastive learning or attention mechanisms to enhance feature extraction and object localization. These advancements have significant implications for enhancing public safety and security by automating a crucial task in various high-throughput security checkpoints, while also providing valuable benchmarks and datasets for further research.