Passenger screeningWinners announced in $1.5 million Passenger Screening Algorithm Challenge
DHS S&T and TSA the other day announced the eight winners of the Passenger Screening Algorithm Challenge. The prize competition solicited new automated detection algorithms from individuals and entities that can improve the speed and accuracy of detecting small threat objects and other prohibited items during the airport passenger screening process.
The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) and the Transportation Security Administration (TSA) the other day announced the eight winners of the Passenger Screening Algorithm Challenge. The prize competition solicited new automated detection algorithms from individuals and entities that can improve the speed and accuracy of detecting small threat objects and other prohibited items during the airport passenger screening process. The competition, which was co-funded by S&T and TSA, awarded a total of $1.5 million to the top eight finishers.
“By reaching beyond the screening equipment industry, we have an opportunity to discover new, non-traditional performers that might otherwise be overlooked,” said William N. Bryan, DHS Senior Official Performing the Duties of Under Secretary for Science and Technology. “Working with algorithm developers to improve screening technologies directly serves S&T’s mission to deliver effective and innovative insight, methods and solutions for the critical needs of the Homeland Security Enterprise.”
S&T says that algorithms developed from this competition have the potential to improve the speed and accuracy of the Advanced Imaging Technology (AIT) scanners used to screen airline passengers for prohibited items. A comprehensive set of new automated detection algorithms has the potential to be integrated into the latest screening equipment.
Jeremy Walthers of Rockville, Maryland, will receive the 1st Place prize of $500,000 for the top scoring algorithm. Walthers’ first place lace approach used an array of deep learning models customized to process images from multiple views.
Sergei Fotin located in Nashua, New Hampshire, will receive the second place prize of $300,000 with an approach that fuses 2D and 3D sources of data to make object and location predictions.
David Odaibo and Thomas Anthony of Alabaster, Alabama, are the winners of the $200,000 third place prize, presenting a solution that uses specialized image level annotations to train their 2-stage identification models.
