AI 中文总结
针对X射线违禁品检测中物体叠加与纹理弱的问题，提出DERA框架，通过分离式边缘残差自适应注入边界先验，仅用14.7K参数在三个数据集上提升基线AP 1.6-3.1点。
AI 中文摘要
由于物体叠加、纹理弱和材料杂乱，X射线图像中的违禁品检测仍然具有挑战性，这些因素掩盖了语义外观和物体边界。我们提出了DERA，一种用于X射线图像下违禁品检测的分离式边缘残差自适应框架。DERA将分层视觉特征与并行像素差分边缘金字塔相结合，并从训练时的实例掩膜轮廓中学习特定于物体的边界先验。分离的先验门控边缘敏感特征，这些特征通过残差头注入早期视觉阶段。这种分阶段设计在自适应开始时保留基础检测器，将边界监督与语义特征学习隔离，并将最终自适应阶段限制为仅14.7K个可训练参数。在PIDray、CLCXray和STCray上评估，DERA分别将基线提高了3.1、1.6和2.4个AP点。
英文摘要
Prohibited-item detection in X-ray imagery remains challenging due to object superposition, weak texture, and material clutter which obscure both semantic appearance and object boundaries. We propose \textbf{DERA}, a \textbf{D}etached \textbf{E}dge-\textbf{R}esidual \textbf{A}daptation framework for prohibited item detection under X-ray imagery. DERA combines hierarchical visual features with a parallel pixel-difference edge pyramid and learns an object-specific boundary prior from training-time contours of the instance masks. The detached prior gates edge-sensitive features, which are injected into the early visual stages through residual heads. This staged design preserves the foundation detector at the start of adaptation, isolates boundary supervision from semantic feature learning, and restricts the final adaptation stage to only \(14.7\)K trainable parameters. Evaluated on PIDray, CLCXray, and STCray, DERA improves the baseline by \textbf{3.1}, \textbf{1.6}, and \textbf{2.4} AP points, respectively.
