DERA: Detached Edge-Residual Adaptation for Prohibited item Detection
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 foundatio...
| Authors | Yonathan Michael, Mohamad Alansari, Mohammed Bennamoun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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