Classifying Aerial Objects Based on Risk: A Machine Learning Approach

Detecting Threats Of Aerial Objects Using Machine Learning

Abstract

The spy balloon that flew across the U.S. in February 2023 posed a serious security threat. US security officials have said this balloon tried to gather intelligence by monitoring sensitive military sites, and as a result, the U.S. government began more closely scrutinizing its airspace to better categorize aerial objects and detect threats. However, the airspace is filled with a myriad of aerial objects, making the problem of classifying and risk determination very challenging. We hypothesize that if we label aerial objects based on the risks they pose, then a Machine Learning algorithm can be made to learn and predict the risks of previously unknown or unseen aerial objects. Currently, there are no known single datasets that contain both old and newer aerial objects, such as drones, planes, etc., nor do datasets have labels to identify the risks associated with the objects. The goal of this research is twofold: 1) We create a new comprehensive dataset that contains traditional and newer aerial objects. We label the objects as high, medium, low, or no risk based on the threat markers on the aerial objects. 2) We use the MobileNetV2 CNN classification algorithm to validate the dataset and provide accurate results. Advancements in this space can potentially help intelligence agencies and security analysts quickly assess developing scenarios and provide a reliable risk assessment for observed aerial objects.