Extended adaptive mutation operator for training an explosive hazard detection prescreener in forward looking infrared imagery

Ravinder Singh; Stanton R. Price; Derek T. Anderson

doi:10.1117/12.2177452

14 May 2015 Extended adaptive mutation operator for training an explosive hazard detection prescreener in forward looking infrared imagery

Ravinder Singh, Stanton R. Price, Derek T. Anderson

Author Affiliations +

Proceedings Volume 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX; 94540L (2015) https://doi.org/10.1117/12.2177452
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

A big challenge with forward looking (FL), versus downward looking, sensors mounted on a ground vehicle for explosive hazard detection (EHD) is they “see everything”, on and off road. Even if a technology such as road detection is used, we still have to find and subsequently discriminate targets versus clutter on the road and often road side. When designing an automatic detection system for FL-EHD, we typically make use of a prescreener to identify regions of interest (ROI) instead of searching for targets in an inefficient brute force fashion by extracting complicated features and running expensive classifiers at every possible translation, rotation and scale. In this article, we explore the role of genetic algorithms (GAs), specifically with respect to a new adaptive mutation operator, for learning the parameters of a FL-EHD prescreener in FL infrared (FLIR) imagery. The proposed extended adaptive mutation (eAM) algorithm is driven by fitness similarities in the chromosome population. Currently, our prescreener consists of many free parameters that are empirically chosen by a researcher. The parameters are learned herein using the proposed optimization technique and the performance of the system is measured using receiver operating characteristic (ROC) curves on data obtained from a U.S. Army test site that includes a variety of target types buried at varying depths and from different times of day. The proposed technique is also applied to numerous synthetic fitness landscapes to further assess the effectiveness of the eAM algorithm. Results show that the new adaptive mutation technique converges faster to a better solution than a GA with fixed mutation.

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Ravinder Singh, Stanton R. Price, and Derek T. Anderson "Extended adaptive mutation operator for training an explosive hazard detection prescreener in forward looking infrared imagery", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540L (14 May 2015); https://doi.org/10.1117/12.2177452

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KEYWORDS

Explosives

Forward looking infrared

Sensors

Explosives detection

Infrared imaging

Infrared radiation

Detection and tracking algorithms

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