DERA: Detached Edge-Residual Adaptation for Prohibited item Detection

2026-09-11

AI Summary

本文提出DERA框架，通过结合层次视觉特征与边缘残差适应方法，解决了X光图像中禁品检测因物体叠加、弱纹理和材质杂乱导致的问题。

Abstract

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.

Problem

• Prohibited item detection
• X-ray imagery
• Object superposition
• Weak texture
• Material clutter

Innovation

• Detached Edge-Residual Adaptation
• X-ray imagery
• boundary prior
• hierarchical visual features
• pixel-difference edge pyramid