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。

MMCL：基于 Deformable DETR 的检测器通过多类别最小边际对比学习提升卓越禁止物品检测

MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class  Min-Margin Contrastive Learning for Superior Prohibited Item Detection

Prohibited item detection in X-ray images is one of the most essential and
highly effective methods widely employed in various security inspection
scenarios. Considering the significant overlapping phenomenon in X-ray
prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on
one of the state-of-the-art general object detectors, DINO. Specifically, to
address the feature coupling issue caused by overlapping phenomena, we
introduce the Category-Specific One-to-One Assignment (CSA) strategy to
constrain category-specific object queries in predicting prohibited items of
fixed categories, which can enhance their ability to extract features specific
to prohibited items of a particular category from the overlapping
foreground-background features. To address the edge blurring problem caused by
overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which
improves the localization accuracy of reference boxes in mid-to-high-level
decoder layers and enhances the ability to locate blurry edges of the final
layer. Similar to DINO, our AO-DETR provides two different versions with
distinct backbones, tailored to meet diverse application requirements.
Extensive experiments on the PIXray and OPIXray datasets demonstrate that the
proposed method surpasses the state-of-the-art object detectors, indicating its
potential applications in the field of prohibited item detection. The source
code will be released at this https URL

禁止项目检测中 X 射线图像的抗重叠 DETR（AO-DETR）是一种基于 DINO 的最新一代目标检测器之一，通过采用类特定一对一分配策略和前向稠密方案解决了重叠现象带来的特征耦合和边缘模糊问题，并在 PIXray 和 OPIXray 数据集上的实验证明了其在禁止项目检测领域的潜在应用。

AO-DETR：针对 X 射线禁止物品检测的反重叠 DETR

AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection

Adversarial attacks are valuable for evaluating the robustness of deep
learning models. Existing attacks are primarily conducted on the visible light
spectrum (e.g., pixel-wise texture perturbation). However, attacks targeting
texture-free X-ray images remain underexplored, despite the widespread
application of X-ray imaging in safety-critical scenarios such as the X-ray
detection of prohibited items. In this paper, we take the first step toward the
study of adversarial attacks targeted at X-ray prohibited item detection, and
reveal the serious threats posed by such attacks in this safety-critical
scenario. Specifically, we posit that successful physical adversarial attacks
in this scenario should be specially designed to circumvent the challenges
posed by color/texture fading and complex overlapping. To this end, we propose
X-adv to generate physically printable metals that act as an adversarial agent
capable of deceiving X-ray detectors when placed in luggage. To resolve the
issues associated with color/texture fading, we develop a differentiable
converter that facilitates the generation of 3D-printable objects with
adversarial shapes, using the gradients of a surrogate model rather than
directly generating adversarial textures. To place the printed 3D adversarial
objects in luggage with complex overlapped instances, we design a policy-based
reinforcement learning strategy to find locations eliciting strong attack
performance in worst-case scenarios whereby the prohibited items are heavily
occluded by other items. To verify the effectiveness of the proposed X-Adv, we
conduct extensive experiments in both the digital and the physical world
(employing a commercial X-ray security inspection system for the latter case).
Furthermore, we present the physical-world X-ray adversarial attack dataset
XAD.

本文针对安全关键场景的 X 光探测中的对禁止物品侦测的对抗攻击进行了第一步探索，提出了能生成可物理打印的金属的 X-adv，以及解决着色 / 纹理褪色问题的可微转换器和可放置在重叠物件行李中的贪心策略的敌对攻击者，最后验证了其在数字和物理世界中的有效性并展示了 XAD 物理世界 X 射线对抗攻击数据集。