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DERA framework enhances prohibited item detection in X-ray imagery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DERA, a new framework designed to improve the detection of prohibited items in X-ray imagery. This method combines hierarchical visual features with a pixel-difference edge pyramid, learning object-specific boundary priors from training data. DERA's architecture allows for adaptation by isolating boundary supervision and limiting trainable parameters to approximately 14.7K. When tested on PIDray, CLCXray, and STCray datasets, DERA demonstrated improvements in average precision by 3.1, 1.6, and 2.4 points, respectively, over baseline models. AI

IMPACT This research could lead to more accurate and efficient security screening systems by improving the detection of concealed items in X-ray scans.

RANK_REASON The cluster is a research paper detailing a new technical framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DERA framework enhances prohibited item detection in X-ray imagery

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The cluster is a research paper detailing a new technical framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yonathan Michael, Mohamad Alansari, Mohammed Bennamoun, Dwarikanath Mahapatra, Andreas Henschel, Naoufel Werghi ·

    DERA: Detached Edge-Residual Adaptation for Prohibited item Detection

    arXiv:2609.12411v1 Announce Type: new 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}etach…

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