Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors
Taimur HassanNaoufel Werghi

Abstract

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and...Continue Reading

References

May 23, 2012·Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine·K Wells, D A Bradley
Feb 6, 2017·Journal of X-ray Science and Technology·Nicolas JaccardLewis D Griffin
Jan 27, 2019·Attention, Perception & Psychophysics·Yanik SterchiAdrian Schwaninger

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Citations

May 20, 2021·Computers in Biology and Medicine·Mehreen SirsharShoab Ahmed Khan

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Methods Mentioned

BETA
X-ray
chip
ranging

Software Mentioned

PCNN
AlexNet
MATLAB
Faster R - CNN
Tiny YOLO
GANomaly
Mask R - CNN
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FAST
DOAM

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