ShayanPersonal/Kaggle-Passenger-Screening-Challenge-Solution
10th place solution to the $1,500,000 Kaggle Passenger Screening Challenge.
This solution helps security screeners or airport operations personnel automatically identify potential threats on passengers. It takes millimeter wave scanner images (16 views of a person) and outputs a probability for each of 17 body zones indicating the presence of a threat. It's designed for use by airport security or operations staff responsible for passenger screening.
No commits in the last 6 months.
Use this if you need a fast, accurate, and resource-efficient way to automatically detect threats in passenger body scans from millimeter wave scanners.
Not ideal if you require pixel-level localization of threats or if you plan to integrate with full 3D body scan data, as this solution focuses solely on 2D views and zone-level detection.
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Language
Python
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Last pushed
Apr 11, 2018
Commits (30d)
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