Detecting Zones and Threat on 3D Body for Security in Airports using Deep Machine Learning
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 airp...
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| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
09-02-2018
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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
https://www.kaggle.com/c/passenger-screening-algorithm-challenge/data 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
preprocessed of the dataset images extracted features from 200 bodies using:
intensity, intensity differences and local neighbourhood to detect, to produce
segmentation regions and label those regions to be used as a truth in a
training and test dataset. The regions are subsequently give to a CNN deep
learning classifier to predict 17 classes (that represents the body zones):
zone1, zone2, ... zone17 and zones with threat in a total of 34 zones. The
analysis showed the results of the classifier an accuracy of 98.2863% and a
loss of 0.091319, as well as an average of 100% for recall and precision. |
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| DOI: | 10.48550/arxiv.1802.00565 |