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Detection of illegal and threatening items in baggage is one of the utmostsecurity concern nowadays. Even for experienced security personnel, manualdetection is a time-consuming and stressful task. Many academics have createdautomated frameworks for detecting suspicious and contraband data from X-rayscans of luggage. However, to our knowledge, no framework exists that utilizestemporal baggage X-ray imagery to effectively screen highly concealed andoccluded objects which are barely visible even to the naked eye. To addressthis, we present a novel temporal fusion driven multi-scale residual fashionedencoder-decoder that takes series of consecutive scans as input and fuses themto generate distinct feature representations of the suspicious andnon-suspicious baggage content, leading towards a more accurate extraction ofthe contraband data. The proposed methodology has been thoroughly tested usingthe publicly accessible GDXray dataset, which is the only dataset containingtemporally linked grayscale X-ray scans showcasing extremely concealedcontraband data. The proposed framework outperforms its competitors on theGDXray dataset on various metrics.

Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats