MP-IST-210-1A.03.pdf — page 6

autonomous drones that do not rely on a control link, and ‘go dark’, i.e. stop broadcasting signals. ASUL’s 
design incorporating a mix of sensors to compensate for each individual sensor’s limitations is an example of 
attempting to overcome this problem at the platform level. However, platform-level limitations may still 
exist, such as the ability to identify whether drones are friendly or hostile without an external validation 
system with additional Identify-Friend-or-Foe (IFF) information. Moreover, handling of both false positives 
(identifying non-drones as threats) and false negatives (failing to detect actual threats) may require decision-
making capabilities beyond the operators and systems on this platform, within the short response window 
typical for aerial threats, may require integration with Command, Control, Computers, Communications, 
Intelligence & Technology (C4IT) systems available on base to ensure decision-makers are able to take the 
best action based on the most complete set of available information. Since any CV tool would ultimately 
form part of a broader defense system, it would be irrational to expect that any system component should 
bear full responsibility for the ultimate outcome. Hence, the focus turns to selecting a suitable set of criteria 
for performance of individual system components, which inevitably runs into the question of what ‘good 
enough’ performance would entail. 
Third, CV systems leveraging machine learning methods exhibit stochasticity in their outputs when deployed 
in the real world because of the ways through which machine learning (ML) algorithms are generated, 
involving stochasticity in data generation, modelling, and optimization. As noted by Sabuncu [12]: 
“stochasticity features in many ML methods, affording them robustness, generalizability, and calibration”. 
However, he also notes that, “the large degree of uncertainty means we have a strong distinction between the 
data used to develop a model (often called training data) and the data used to objectively assess performance 
(the validation and test datasets), sometimes under different conditions. Most ML models exhibit poorer 
performance on independent data than on training data - a phenomenon called overfitting. Overfitting, or the 
generalization gap (performance difference between training and test data), is one of the core challenges of 
ML.” Burton and Herd (ibid.) asserts that uncertainty arising from attempting to assure such applications 
“can thus manifest itself as a lack of knowledge of the difference between the estimated failure rate … and 
the actual failure rate that occurs during operation. This “assurance gap” will typically need to be closed 
based on a combination of quantitative (e.g., related to statistical confidence) and qualitative arguments (e.g., 
based on the appropriateness of certain assumptions). [2]” Hence, the evaluation of CV systems should aim 
not to provide a definitive and deterministic view of how they would perform in deployment, but rather 
provide a sufficient confidence in having a stochastic outcome that would be acceptable given the level of 
risk tolerated by their employers. The attendant methodology must thus consider having sufficient and 
diverse test points to fulfil the requirements for such statistical confidence. 
2.2 
Limitations of Existing Methods 
Existing evaluation methods for CV systems, particularly in critical applications like counter-drone 
technology, present significant limitations. Current approaches primarily rely on two main categories: public 
benchmarks and organization or project-centric evaluations. Each of these methods, while offering some 
benefits, comes with inherent problems that hinder a comprehensive and trustworthy assessment framework. 
Public benchmarks offer a transparent view of CV system performance across a standardized dataset but 
have seen limited growth in adoption thus far. On the other hand, organization/project-centric evaluations 
presumably generate higher impact for their sponsoring organizations but are rarely disclosed to other 
ecosystem stakeholders outside the project or organization due to confidentiality or security considerations.  
2.2.1 
Approach and Limitations of Public Benchmarks 
Public benchmarks attempt to provide a standardized platform for comparison across different algorithms 
and models, often with the intention of furthering research and development in this space through convening 
different research groups to optimise their algorithms along a similar baseline and dataset. Yifei Dong et al. 
found in a seminal comprehensive literature review a wide-ranging overview of the benchmarking landscape 
benchmarks with single and multi-modal datasets ranging across electro-optical red-green-blue (EO/RGB), 
Assuring Trustworthy Computer Vision 
for Rapid Counter-Drone System Testing and Deployment 
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STO-MP-IST-210