Aerial multi-spectral AI-based detection system for unexploded ordnance

Abstract

Unexploded ordnance (UXO) poses a threat to soldiers operating in mission areas, but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard. Recent technological advancements in artificial intelligence (AI) and small unmanned aerial systems (sUAS) present an opportunity to explore a novel concept for UXO detection. The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral (MS) sensor on sUAS. This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single (visible) spectrum (SS) or MS digital electro-optical (EO) sensor. Specifically, it describes the design of the Deep Learning Convolutional Neural Network for UXO detection, the development of an AI-based algorithm for reliable UXO detection, and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.

Keywords

• Unexploded ordnance (UXO)
• Multispectral imaging
• Small unmanned aerial systems (sUAS)
• Object detection
• Deep learning convolutional neural network (DLCNN)