{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detecting-zones-and-threat-on-3d-body-for","title":"Detecting Zones and Threat on 3D Body for Security in Airports using Deep Machine Learning","arxiv_id":"1802.00565","date":"2018-02-02","proceeding":null,"authors":["Abel Ag Rb Guimaraes","Ghassem Tofighi"],"abstract":"In this research, it was used a segmentation and classification method to\nidentify threat recognition in human scanner images of airport security. The\nDepartment of Homeland Security's (DHS) in USA has a higher false alarm,\nproduced from theirs algorithms using today's scanners at the airports. To\nrepair this problem they started a new competition at Kaggle site asking the\nscience community to improve their detection with new algorithms. The dataset\nused in this research comes from DHS at\nhttps://www.kaggle.com/c/passenger-screening-algorithm-challenge/data According\nto DHS: \"This dataset contains a large number of body scans acquired by a new\ngeneration of millimeter wave scanner called the High Definition-Advanced\nImaging Technology (HD-AIT) system. They are comprised of volunteers wearing\ndifferent clothing types (from light summer clothes to heavy winter clothes),\ndifferent body mass indices, different genders, different numbers of threats,\nand different types of threats\". Using Python as a principal language, the\npreprocessed of the dataset images extracted features from 200 bodies using:\nintensity, intensity differences and local neighbourhood to detect, to produce\nsegmentation regions and label those regions to be used as a truth in a\ntraining and test dataset. The regions are subsequently give to a CNN deep\nlearning classifier to predict 17 classes (that represents the body zones):\nzone1, zone2, ... zone17 and zones with threat in a total of 34 zones. The\nanalysis showed the results of the classifier an accuracy of 98.2863% and a\nloss of 0.091319, as well as an average of 100% for recall and precision.","url_abs":"http://arxiv.org/abs/1802.00565v2","url_pdf":"http://arxiv.org/pdf/1802.00565v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detecting-zones-and-threat-on-3d-body-for","repo_url":"https://github.com/abelguima/ryerson-capstone-CKME136","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}