Recently, blind tests of several automated detection algorithms operating on the NIITEK ground penetrating radar data (GPR) have resulted in quite promising performance results. Anecdotally, human observers have also shown notable skill in detecting landmines and rejecting false alarms in this same data; however, the basis of human performance has not been studied in depth. In this study, human observers are recruited from the undergraduate and graduate student population at Duke University and are trained to visually detect landmines in the NIITEK GPR data. Subjects are then presented with GPR responses associated with blanks, clutter items (including emplaced clutter), and landmines in a blind test scenario. Subjects are asked to make the decision as to whether they are viewing a landmine response or a false alarm, and their performance is scored. A variety of landmines, measured at several test sites, are presented to determine the relative difficulty in detecting each mine type. Subject performance is compared to the performance of two automated algorithms already under development for the NIITEK radar system: LMS and FROSAW. In addition, subjects are given a subset of features for each alarm from which they may indicate the reason behind their decision. These last data may provide a basis for the design of an automated algorithm that takes advantage of the most useful of the observed features.
Sensors;Data processing;Alarm systems;Sustainable development;Bandwidth;Algorithms;