Baggage Inspection

Recognition of Prohibited Objects in Baggage Inspection using 3X Strategy

In this project, we propose an unified baggage inspection strategy that we call the `3X-strategy’ that uses a combination of three research areas: X1  (energies: mono-energy, dual-energy and multi-energy), X2 (views: single view, multiple views and computed tomography) and X3 (algorithms: of low, medium and high complexity) that can be used in detection processes. We believe that for many threat objects there can be a suitable combination of them. The focus of our research is how robust X-ray testing can be performed with some degree of generality. Our general goal is to contribute to efforts to improve the effectiveness of baggage inspection by using an ad-hoc 3X-combination.

Intelligent Baggage Inspection System using X-ray Images and Deep Learning

X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. This project attempts to make a contribution to the field of object recognition in X-ray testing by evaluating different computer vision strategies based on deep learning that have been proposed in the last years. We strongly believe that it is possible to design an automated aid for the human inspection task using these computer vision algorithms.

A Logarithmic X-ray Imaging Model for Baggage Inspection: Simulation and Object Detection. Mery, D.; and Katsaggelos, A. IEEE CVPR Workshop on Perception Beyond the Visible Spectrum (PBVS 2017). 

Threat Objects Detection in X-ray Images Using an Active Vision Approach. Riffo, V.; Flores, S.; and Mery, D. Journal of Nondestructive Evaluation, 2017. 

Modern Computer Vision Techniques for X-ray Testing in Baggage Inspection. Mery, D.; Svec, E.; Arias, M.; Riffo, V.; Saavedra, J.; and Banerjee, S. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017. 

Object recognition in X-ray testing using an efficient search algorithm in multiple views. Mery, D.; Riffo, V.; Zuccar, I.; and Pieringer, C. Insight – Non-Destructive Testing and Condition Monitoring, 2017.