MP-IST-210-1A.03.pdf — page 2

Assuring Trustworthy Computer Vision 
for Rapid Counter-Drone System Testing and Deployment 
made significant advancements from the increasing adoption of techniques in artificial intelligence (AI) 
including machine learning, deep learning, and pattern recognition. Accordingly, CV systems have also seen 
widespread use in defense applications and systems, augmenting human operators in processing and 
analyzing data from a wide variety of imaging sensors for target detection, classification, tracking, and 
prosecution. Applied to the counter-drone defense mission, CV systems can be useful in (a) extending 
operators’ ability to perceive small-sized drones beyond relying on human sight, (b) ease classification, 
tracking and prosecution tasks through computer automation and therefore handle more challenging targets, 
(c) compensate for the other counter-drone sensors’ limitations and enhance system redundancy, at the
expense of increased computational requirements and system complexity. In this paper, the term 'drone'
refers to a flying vehicle without a pilot and should be understood as interchangeable with ‘unmanned aerial
vehicle’ (UAV). The counter-drone mission specifically focuses on detecting, classifying, tracking, and
targeting these drones/UAVs, excluding their ground control systems, pilots and support crew and their
equipment, and other unmanned aerial system (UAS) components.
2.0 RATIONALE 
The rapid proliferation of drones across the inventories of potential adversaries, spurred by advances in 
commercial and recreational applications but adapted for military uses, has concurrently given rise to a 
critical challenge: the need for effective counter-drone measures. This escalating drone adoption has spurred 
the development of numerous solutions aimed at detecting, tracking, and neutralizing unauthorized or hostile 
drones. However, the sheer variety and complexity of these emerging technologies present significant 
hurdles for decision-makers tasked with their deployment. The process of selecting the most appropriate 
counter-drone systems for their specific missions involves a deep understanding of each system's 
capabilities, limitations, and operational requirements. Beyond selection, there is also a need to comprehend 
how best to integrate and employ these tools within existing security frameworks, which necessitates 
detailed planning for system placement, operational protocols, and personnel training to maximize 
effectiveness. 
Crucially, decision-makers must also anticipate and understand the circumstances under which their chosen 
systems might perform sub-optimally or even fail entirely. Simon Burton and Benjamin Herd from the 
Fraunhofer Institute for Cognitive Systems in Germany note that “the role of safety assurance can be seen as 
striving to facilitate decisions of type II wherever type I is not possible, whilst avoiding type III decisions” 
[2], in reference to Knight’s “three types of decisions: decisions under certainty (type I) where the 
consequences of all options are known; decisions under risk (type II) where possible futures are known, 
probability distributions are known, and statistical analysis is possible; and decisions under uncertainty (type 
III) where the future states are known but the probabilities are unknown” [8]. For decision-makers in defense
and security, these activities of safety assurance are also relevant to objectives outside of system safety, such
as the ability to complete the assigned mission, and mitigating the impact on manpower and materiel in
operations should complications arise; recognizing these potential shortcomings is vital for designing
redundancy in mission-critical systems, and robust contingency plans. These workarounds might involve the
deployment of alternative tools, such as different sensor types or kinetic countermeasures, or the devising of
innovative operational strategies that leverage human intervention or other available resources to compensate
for system limitations.
Any proposed counter-drone solution, whether a standalone system or a combination of technologies and 
operational procedures, must undergo a comprehensive assessment of its associated costs and risks. This 
includes not only financial and manpower investments in procurement, maintenance and operations, but also 
the potential for collateral damage, legal implications, and public perception. The objective is to arrive at a 
suitable outcome that effectively mitigates drone-related threats while ensuring overall mission success. 
Therefore, there is a clear imperative to establish a performance measure for these counter-drone systems, 
especially for stochastic elements within them such as CV or other AI models. This performance 
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