Detecting zones and threat on 3D body in security airports using deep learning machine (original) (raw)

2018

Abstract

In this research, it was used a segmentation and classification method to identify threat recognition in human scanner images of airport security. The Department of Homeland Security's (DHS) in USA has a higher false alarm, produced from theirs algorithms using today's scanners at the airports. To repair this problem they started a new competition at Kaggle site asking the science community to improve their detection with new algorithms. The dataset used in this research comes from DHS at this https URL According to DHS: "This dataset contains a large number of body scans acquired by a new generation of millimeter wave scanner called the High Definition-Advanced Imaging Technology (HD-AIT) system. They are comprised of volunteers wearing different clothing types (from light summer clothes to heavy winter clothes), different body mass indices, different genders, different numbers of threats, and different types of threats". Using Python as a principal language, the ...

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