Using machine learning to detect and localize concealed objects in passive millimeter-wave images. (January 2018)

• Record Type: Journal Article
• Authors: López-Tapia, Santiago; Molina, Rafael; Pérez de la Blanca, Nicolás
• Is Part Of: Engineering applications of artificial intelligence. Volume 67(2018:Jan.)
• Page Start: 81
• Page End: 90
• Publication Date: 2018-01
• Subjects: Threat detection -- Machine learning -- Passive millimeter-wave imaging; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.09.005
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The detection and location of objects concealed under clothing is a very challenging task that has crucial applications in security. In this domain, passive millimeter-wave images (PMMWIs) can be used. However, the quality of the acquired images, and the unknown position, shape, and size of hidden objects render this task difficult. In this paper, we propose a machine learning-based solution to this detection/localization problem. Our method outperforms currently used approaches. The effect of non-stationary noise on different classification algorithms is analyzed and discussed, and a detailed experimental comparative study of classification techniques is presented using a new and comprehensive PMMWI database. The low computational testing cost of this solution allows for its use in real-time applications. Highlights: A new approach to the detection of hidden objects in PMMWI based on machine learning. A comparative experimental study between two type of features and six classifiers. A new database of Passive Millimeter Wave Images (PMMWI).