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Rapport (Rapport De Recherche) Année : 2021

Automated Threat Detection In X-Ray Imagery For Advanced Security Applications

Amit Upreti
  • Fonction : Auteur
  • PersonId : 1121165
Bothra Rajat
  • Fonction : Auteur
  • PersonId : 1121166

Résumé

In areas of high security, like airports, etc, X-Ray machines are used to scan baggage to look for hazardous objects such as guns, knives, razor blades. But, in a manual scan, it is easy to miss some details. Also manual scan is a tedious and time-consuming process. We investigate and compare several algorithms for detection of harmful/hazardous objects like razor blades/handguns etc in X-Ray images of travellers' baggage. Deep Convolutional neural networks such as RCNN, Detectron, RetinaNet and Yolo has shown great results in object detection and recognition. We plan to use the object detection techniques and apply them to improvise upon the already existing Automatic baggage screening methods.
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Dates et versions

hal-03501121 , version 1 (23-12-2021)

Identifiants

  • HAL Id : hal-03501121 , version 1

Citer

Amit Upreti, Bothra Rajat. Automated Threat Detection In X-Ray Imagery For Advanced Security Applications. [Research Report] University of Alberta,Canada. 2021. ⟨hal-03501121⟩

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