Multiple Instance Learning for Buried Hazard Detection

摘要

Buried explosives hazards are one of the many deadly threats facing our Soldiers, thus the U.S. Army is interested in the detection and neutralization of these hazards. One method of buried target detection uses forward-looking ground-penetrating radar (FLGPR), and it has grown in popularity due to its ability to detect buried targets at a standoff distance. FLGPR approaches often use machine learning techniques to improve the accuracy of detection. We investigate an approach to explosive hazard detection that exploits multi-instance features to discriminate between hazardous and non-hazardous returns in FLGPR data. One challenge this problem presents is a high number of clutter and non-target objects relative to the number of targets present. Our approach learns a bag of words model of the multi-instance signatures of potential targets and confuser objects in order to classify alarms as either targets or false alarms. We demonstrate our method on test data collected at a U.S. Army test site.