The Transportation Security Agency plans to incorporate machine learning into the computer tomography scanners that are starting to be used at airport security checkpoints. TSA methods in this aspect is adopted by airport security regulators around the world.
To advance the Accessible Property Screening Systems program, TSA is looking for researchers and industry partners to develop algorithms that could improve the automated detection of explosives and prohibited items among carry-on baggage and speed passengers through the checkpoints. Although it has been used to screen checked luggage for explosives since 2001, CT scanning is a relatively new tool for examining carry-on baggage where it would have to identify prohibited items like knives and disassembled weapons. The 3-D imaging and detection software in the CT scanners would increase the speed and accuracy of the scans, flagging the threats operators should manually check. It may eliminate the need for passengers to put their electronics and liquids in separate screening bins.
Skyrus AIBS is a 3D imaging and detection software with an embedded algorithm that can digitally filter the relevant prohibited objects in any carry-on or check-in baggage. Our machine learning algorithm understands 3D imaging of objects (including liquids or gaseous) and these objects are analyzed by the algorithm and the algorithm can decides, if the baggage must be physically inspected or cleared to go. The Skyrus AIBS solution can efficiently clear baggage in a short span of time, therefore reducing workforce in the inspection chambers.