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DETECTING PROHIBITED ITEMS IN X-RAY IMAGES: A CONTOUR PROPOSAL LEARNING APPROACH

Taimur Hassan, Meriem Bettayeb, Samet Akcay, Salman Khan, Mohammed Bennamoun, Naoufel Werghi

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    Length: 09:47
28 Oct 2020

X-ray baggage screening plays a vital role in aviation security. Manual inspection of potentially anomalous items is challenging due to the clutter and occlusion within X-ray scans. Here, we address this issue by presenting an object-boundaries driven framework for the automated detection of suspicious items from X-ray baggage scans. Rather than recognizing objects directly from the X-ray images, our two-stage detection approach first extracts contour-based proposals using a novel cascaded structure tensor technique and subsequently passes the candidate proposals to a single feed-forward convolutional neural network for recognition. Thorough experimentation on GDXray and SIXray datasets demonstrates that the proposed model achieves a mean area under the curve of 0.9878, outperforming the existing renown state-of-the-art object detection frameworks.

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