Convolutional Networks for Classification of Mortars

Abstract

Abstract The task of classifying unexploded mortars is critical in both humanitarian and military explosive ordnance disposal (EOD) operations. Classification needs to be completed quickly and accurately and is the first step toward disarming the ordnance because it provides information about the fuzing mechanism, or the stage in the arming cycle that the ordnance is currently in. To assist EOD technicians with mortar identification, this article presents an automated image-based algorithm and the database of images used in its development. The algorithm utilizes convolutional networks with variations to training to improve performance for ordnance found in varying states of disassembly. The classifier developed was found to be 98.5% accurate for these lab condition photos; future work will focus on more cluttered environments.